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Dissertations, Theses, and Professional Projects
Cortical Involvement During Sustained Lower
Limb Contractions
Marnie Lynn Vanden Noven
Marquette University
Recommended Citation
Vanden Noven, Marnie Lynn, "Cortical Involvement During Sustained Lower Limb Contractions" (2014). Dissertations (2009 -).
Paper 363.
http://epublications.marquette.edu/dissertations_mu/363
CORTICAL INVOLVEMENT DURING SUSTAINED LOWER LIMB
CONTRACTIONS
By
Marnie Lynn Vanden Noven
A Dissertation submitted to the Faculty of the Graduate School,
Marquette University,
in Partial Fulfillment of the Requirements for
the Degree of Doctor of Philosophy
Milwaukee, Wisconsin
May 2014
ABSTRACT
CORTICAL INVOLVEMENT DURING SUSTAINED LOWER LIMB
CONTRACTIONS
Marnie Lynn Vanden Noven
Marquette University, 2014
Despite the critical role of the lower limb during functional tasks such as walking,
most studies examining the role of the cortex during muscle contractions have been
conducted in upper limb muscles. Modulation of force by the cortex in the lower
extremity and the influence of cortical inputs are poorly understood. The purpose of this
dissertation was to investigate the role the cortex plays in modulating force control during
static contractions with the lower limb and to determine the influence of manipulating
cortical inputs.
Aim 1 determined the cortical regions involved in force-related changes between
low and high forces and those areas that modulate steadiness (force fluctuations) during
sustained isometric ankle dorsiflexion contractions in young men and women. This was
achieved using functional magnetic imaging (fMRI). Both motor and some typically
associated non-motor brain areas were active during lower limb force production and
scaled linearly as force increased. Steadiness was associated with both motor and nonmotor brain areas with minimal differences in areas activated between men and women.
Aim 2 examined the influence of cognitive demand (null, low-cognitive demand, highcognitive demand) on fatigability and steadiness of low- to moderate-force isometric
contractions in young and older men and women. Women demonstrated greater force
fluctuations than men during both the low- and moderate-force contractions and their
motor output was influenced by changes in cognitive demand. Older adults were less
steady than young during low- and moderate-force contractions, had greater age-related
reductions in steadiness, and greater variability in fatigability when cognitive demand
was increased.
This dissertation shows that cortical inputs are very important to lower limb motor
control of static voluntary contractions. Cortical motor and non-motor regions that are
important for control of force intensity and steadiness of lower limb contractions were
identified and are key areas for potential cortical plasticity with impaired or enhanced leg
function. Steadiness was altered by increasing cortical inputs (cognitive demand)
especially in older adults whose motor performance was impaired and more variable than
young. These results have important performance implications for cognitively
demanding and low- to moderate-force tasks that are common to daily function in older
adults.
i
ACKNOWLEDGMENTS
Marnie Lynn Vanden Noven
First and foremost, I would like to thank Dr. Sandra Hunter. I am honored to
have had you not only as my mentor, but as my friend. You have taught me so much
over the course of this journey, most of it outside of the lab. Thank you for your time,
energy, unwavering support, generosity, kindness and the not-so-occasional box of
tissues. Thank you also to your family, Jeff and Kennedy Rainwater. Thank you for
welcoming me into your home and allowing me to break bread, sleep under your roof,
and study at your dining room table. All three of you went above and beyond to help me
make this happen. It wouldn’t have happened without you. Thank you.
Thank you also to my committee – Dr. Lawrence Pan, Dr. Kristy Nielson, Dr.
Alex Ng and Dr. Richard Marklin. I am so thankful that you were willing to work with
me throughout this process. I appreciate your wisdom, support and the time you took out
of your already busy schedules to make this happen. I enjoyed working with you.
Thank you to my friends and colleagues at Marquette University. There are too
many of you to name you individually because you are such a fantastic, supportive
group! Thank you for all of your smiles, supportive words and enthusiasm. I miss seeing
you in the halls and on campus, sharing stories, and your hugs! I am blessed to have had
the opportunity to meet you, work with you and call you my friends. Thank you also to
all of the Graduate and Undergraduate students that helped with experiments, data
collection, data analysis, posters, and the list goes on. I am honored to have worked with
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you! Thank you to Craig Pierce and the staff at the Graduate School. Thank you to
Hugo Pereira and Tejin Yoon for your patience with me and your help with data analysis.
A special thanks to Dr. Manda Keller-Ross for paving the way and mentoring me through
the hardest part!
Thank you to everyone at the Medical College of Wisconsin and Belmont
University for your support in making this PhD a reality.
Thank you also to my PhD sisters Celeste Harvey, Dora Jones, and Felisa Parris.
Chance brought us together, but friendship and a common goal kept us together! Thank
you for pushing me when I needed it, picking me up when I was down, and reminding me
to keep my eye on the prize! You are a blessing! We will all cross that stage!
Thank you to NIOSH for grant funding to support study 2 and 3. Thank you also
the College of Health Sciences for your support to complete the functional imaging at the
Medical College of Wisconsin.
Finally, and most important of all, I would like to thank my husband Steve
Vanden Noven. Many years ago, I told him about my dream of getting my PhD. He has
supported me every step of the way and has been my biggest champion. Thank you for
believing in me and knowing that we would get this done together. Thank you for all of
the times you left work early, got to work late, and weekends you steered the ship. I am
blessed to have you as mine. Thank you to Steven and Sophia Vanden Noven for their
patience (most of the time) and understanding how important this PhD is to me. I am
proud of you and can’t wait to play! Thank you to my parents, George and Stella Brown,
for teaching me the importance of an education and working hard for something you
want. I love you all.
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TABLE OF CONTENTS
ACKNOWLEDGMENTS…………………………………………………………...........i
LIST OF TABLES.............................................................................................................iv
LIST OF FIGURES............................................................................................................v
LIST OF ABBREVIATIONS...........................................................................................vii
CHAPTER
I. INTRODUCTION......................................................................................1
II. BRAIN AREAS ASSOCIATED WITH FORCE STEADINESS AND
INTENSITY DURING ISOMETRIC ANKLE DORSIFLEXION IN MEN
AND WOMEN..........................................................................................27
III. WOMEN ARE LESS STEADY THAN MEN DURING LOW-FORCE
ISOMETRIC CONTRACTIONS OF THE LOWER
EXTREMITY............................................................................................57
IV. MOTOR VARIABILITY DURING SUSTAINED CONTRACTIONS
INCREASES WITH COGNITIVE DEMAND IN OLDER
ADULTS....................................................................................................87
V. DISCUSSION..........................................................................................119
BIBLIOGRAPHY............................................................................................................126
iv
LIST OF TABLES
CHAPTER I. INTRODUCTION
Table 1.1. Cortical regions of interest...................................................................10
CHAPTER II. BRAIN ACTIVATION DURING LOWER LIMB CONTRACTIONS
Table 2.1. Brain activation areas...........................................................................40
Table 2.2. Brain Areas with Increased Activation Volume...................................45
Table 2.3. Torque fluctuations (SD) and PSC: Correlations................................48
Table 2.4. Torque fluctuations (CV) and PSC: Correlations................................48
CHAPTER III. SEX DIFFERENCES IN LOWER LIMB WITH COGNITIVE DEMAND
Table 3.1. Participant Physical Characteristics and Results.................................63
CHAPTER IV. MOTOR VARIABILITY WITH AGING
Table 4.1. Participant Physical Characteristics and Results.................................94
v
LIST OF FIGURES
CHAPTER I. INTRODUCTION
Figure 1.1. Somatotopic Representation in the Primary Motor Cortex..................4
Figure 1.2. Standard Deviation and Coefficient of Variation of Force................14
Figure1.3. Characteristics of a Force Task Performed to Failure..........................18
CHAPTER II. BRAIN ACTIVATION DURING LOWER LIMB CONTRACTIONS
Figure 2.1. Experimental Protocol.........................................................................33
Figure 2.2. Torque and Steadiness........................................................................38
Figure 2.3. Brain Activation Areas during Right Ankle Isometric Dorsiflexion.41
Figure 2.4. Percent Signal Change in Regions of Interest.....................................44
CHAPTER III. SEX DIFFERENCES IN LOWER LIMB WITH COGNITIVE DEMAND
Figure 3.1. Experimental Protocol.........................................................................64
Figure 3.2. State STAI scores................................................................................75
Figure 3.3. Visual Analogue Scale (VAS) Scores for Anxiety and Stress...........76
Figure 3.4. Mean Session Coefficient of Variation (CV) of Torque during the 5%
MVC Task..............................................................................................................77
Figure 3.5. Time to Task Failure during the 30% MVC Task..............................79
Figure 3.6. Coefficient of Variation (CV) of Force during the Fatiguing
Contraction.............................................................................................................80
CHAPTER IV. MOTOR VARIABILITY WITH AGING
Figure 4.1. Visual Analogue Scale for Anxiety (A) and Stress (B).......................97
Figure 4.2. Mean Session Coefficient of Variation of Torque..............................99
Figure 4.3. Individual Times to Task Failure during the Fatiguing Contraction.102
vi
LIST OF FIGURES, continued
Figure 4.4. Coefficient of Variation of Force during Fatiguing Contraction.......104
Figure 4.5. Mean Arterial Pressure, Heart Rate and Rate Pressure Product.......107
vii
LIST OF ABBREVIATIONS
AFNI = Analysis of Functional NeuroImages
ANOVA = Analysis of Variance
BOLD = Blood-Oxygen-Level Dependent
C = Coronal
CG = Cingulate Gyrus
CV = Coefficient of Variation
DBP = Diastolic Blood Pressure
EMG = Electromyography
fMRI = Functional Magnetic Resonance Imaging
FWHM = Full Width at Half Maximum
GPe = Globus Pallidus external
GPi = Globus Pallidus internal
High-CD = High-Cognitive Demand
I = Inferior
L = Left
Low-CD = Low-Cognitive Demand
M1 = Primary Motor Area
MAP = Mean Arterial Pressure
MVC = Maximal Voluntary Contraction
PAQ = Physical Activity Questionnaire
PFC = Prefrontal Cortex
viii
PSC = Percent Signal Change
R = Right
RIFG = Right Inferior Frontal Gyrus
RMS = Root Mean Square
ROI = Region of Interest
RPP = Rate Pressure Product
RPE = Rating of Perceived Exertion
Su = Superior
S = Sagittal
TE = Echo Time
TR = Repetition Time
T-T = Talaraich and Tournoux
SBP = Systolic Blood Pressure
SD = Standard Deviation
SEM = Standard Error Measurement
SMA = Supplemental Motor Area
STAI = State Trait Anxiety Inventory
VAS = Visual Analog Scale
Z = Axial
1
INTRODUCTION
Although steady, controlled movements in the lower extremity are imperative
when performing a basic functional activity such as walking, the role of the cortex in
producing and controlling movements in the lower extremity is poorly understood.
Because most studies investigating the role of the cortex in movement production have
been performed in the upper extremity (Dai et al., 2001; eg., Thickbroom et al., 1998;
Vaillancourt et al., 2004; van Duinen et al., 2008), the purpose of this dissertation was to
determine the influence of cortical input on force control and fatigability during static
contractions in the lower extremity.
When performing a task under more challenging conditions, such as walking
uphill or on an uneven surface, which requires increased force production and greater
steadiness to avoid falling, the cortex must modulate force to accommodate the increased
force requirements of the task. Lower limb control during static contractions differs from
that of the upper limb (eg., Jesunathadas et al., 2012) because lower limb muscles usually
have a larger muscle mass and motor unit ratio (Feinstein et al., 1955), and fewer direct
corticospinal connections (Brouwer & Ashby, 1990) than upper limb muscles. Thus,
aim 1 (study 1) in this dissertation determined the cortical regions involved in forcerelated changes between low and high forces, and modulations of steadiness (force
fluctuations) using fMRI during sustained isometric ankle dorsiflexion contractions in
young men and women.
During a sustained target force contraction, force fluctuations increase as the
muscle becomes more fatigued (Hunter, Duchateau, et al., 2004). In an effort to sustain
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the target force, descending cortical drive will increase (Fuglevand, A. J. et al., 1993).
While descending inputs from cortical centers to the motoneurone pool play a critical role
in modulation of steadiness and fatigability (Dideriksen et al., 2012; Negro et al., 2009),
many of these studies have implicated the role of descending drive with modeling
because of the difficulty in studying the intact central nervous system in humans.
Recent studies show that in young adults, increased cognitive demand will
increase fatigability (decrease time to failure of a sustained contraction) and decrease
steadiness (increase force fluctuations) in the arm muscles (Keller-Ross, Pruse, et al.,
2014; Yoon et al., 2009); the response to increased cognitive demand appears to be
exacerbated in older adults (Christou et al., 2004; Marmon, Pascoe, et al., 2011). Aim 2
exposed the role and importance of the cortical centers during lower limb static
contractions through two studies. The first study of aim 2, varied the level of cognitive
demand and determined the change in motor output (steadiness and fatigability) in young
men and women (study 2). In the second study of aim 2, we determined if age-related
changes in cortical centers in older men and women would further disrupt motor output
during static contractions of the lower extremity muscles (study 3). Thus, aim 2 examined
the influence of cognitive demand (null, low-cognitive demand, high-cognitive demand)
on fatigability and steadiness of low- to moderate-force isometric contractions in young
and older men and women.
3
CHAPTER 1
BACKGROUND
Cortical control of voluntary motor tasks with human muscles has been widely
studied and yet is still not well understood. This is partly due to the limitations with
accessing the central nervous system in awake humans during voluntary movement. In
1968, Evarts published a series of articles showing that the cerebral cortex of macaque
monkeys, specifically the pyramidal tract neurons that originate in the motor cortex, were
directly involved in wrist flexion and extension force production and direction of
displacement. Interestingly, however, despite their role in force production, the
pyramidal tract neurons were not responsible for sensing the amount of force needed to
perform the movement, indicating that this must occur somewhere else in the brain
(Evarts, 1968). Although many of the studies examining motor control have been
performed using the upper limb muscles, the exact role of the motor cortex in
force/torque production, limb position, velocity and direction of the upper limb still
remain unclear (Hatsopoulos, 2005; Sergio et al., 2005).
Movement of a limb is very complex and vulnerable to feedback from multiple
systems. Voluntary contractions are initiated in cortical centers upstream of motor
centers; however, descending drive to the final common pathway (the motor unit)
(Sherrington, 1925) is modulated by inputs from other cortical centers, afferent inputs,
reflexes, the excitatory and inhibitory spinal circuitry, and the characteristics of the motor
unit itself (Enoka & Pearson, 2012). The motoneurone originates in the spinal cord
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(Squire et al., 2013) such that some inputs from corticospinal connections are direct to the
motor unit pools while others are modulated via upper motorneurones and input to
interneurons via upper motoneurones. The upper limb has more direct corticospinal
connections than the lower limb (Brouwer & Ashby, 1990), therefore the area active in
the brain during upper limb activity has a larger representation and is probably influenced
more directly by cortical inputs (Figure 1.1).
A
B
C
Figure 1.1. Somatotopic representation in the primary motor cortex. Panel (A) illustrates
somatotopic order for feet, fist, and lip movements, and (B) is a somatotopic representation for
lip, index finger, thumb, elbow, foot, and left fist movement (Lotze et al., 2000). (C) Organization
of the primary motor cortex in the homunculus (Squire et al., 2013, p. 607).
Fine control of discrete voluntary movements, such as reaching, requires all
information descending from supraspinal centers to be integrated at the spinal level with
afferent feedback to control and voluntarily modify the task towards successful
5
completion (Drew et al., 2004). Current techniques utilized to record and measure neural
activity during discrete motor tasks provide valuable information regarding temporal
activity [e.g., electromyography (EMG)] as well as spatial activity (e.g., fMRI) of
motoneurones; however, very little in known about cortical contributions to lower
extremity movements. Thus, the first study of this dissertation investigates force-related
changes in cortical activity across a range of force intensities during sustained isometric
ankle dorsiflexion contractions in young men and women.
To perform a basic motor task, such as locomotion in the lower limb, the human
body employs the use of specific neural networks that are responsible for executing
motor programs. These neural networks communicate via groups of interneurons (central
pattern generators) with motoneurones to activate the desired movement through
activation of agonistic and synergistic muscle groups and inhibition of antagonistic
muscle groups (Squire et al., 2013). These specific motor programs are generally located
in the spinal cord and brain stem and are capable of producing motor patterns like gait
without input from the cortex as exhibited by decerebrate cat preparations (Van de
Crommert et al., 1998; Whelan, 1996). Although sensory input from the environment
enables modification of motor program patterns (Whelan, 1996), coordination of more
complex, goal-directed movements, requires input from other regions of the central
nervous system including the sensorimotor cortex and basal ganglia (Burke et al., 2001;
Grillner et al., 2008; Heckman et al., 2009).
When attempting to sustain a steady contraction, the exerted muscle force tends
to fluctuate about the target force (eg., Galganski et al., 1993). The amplitude of force
fluctuations (steadiness) can vary between men and women, and the magnitude of the
6
difference will depend on the conditions of the task. For example, women were less
steady than men when performing elbow flexion contractions in multiple forearm
positions across several force levels for a target-matching task especially at very low
forces (Brown et al., 2010); yet, when performing a position task, force task at a fixed
1Newton (N), and precision grip task at a fixed 2N using the first dorsal interosseus,
women were more steady than men (Endo & Kawahara, 2011). In a study that compared
sex differences in hip extension and hip flexion at very low-to-moderate forces, women
were less steady than men at the very low forces during hip extension, but there was no
difference during hip flexion (Grunte et al., 2009). Attempting to sustain a task in the
presence of increased cognitive demand may influence descending drive from the central
nervous system and impair motor output of the upper extremity muscles (Christou et al.,
2004; Noteboom, Fleshner, et al., 2001; Yoon et al., 2009).
Motor output can also be altered by the age-related changes within the
neuromuscular system. As individuals age, there is a loss of motoneurones, which
increases the innervation ratio of surviving motor units and the density of muscle fibers
innervated by a single motor unit (Drey et al., 2013; Kaya et al., 2013). As a result,
steadiness is reduced in the aging adult compared with younger adults when performing
sustained isometric contractions at low and moderate force intensities (Galganski et al.,
1993).
With the loss of motoneurones, older adults also experience a preferential loss of
fast-twitch muscle fibers, increasing resistance to muscle fatigability. When compared
with young adults, older adults had a longer time to task failure than young adults during
static contractions with the ankle dorsiflexor muscles (Griffith et al., 2010). Further,
7
older adults demonstrated a greater increase in force fluctuations than young adults in
anticipation of a noxious stressor for a precision grip task (Christou et al., 2004); however
the influence of increased arousal (cognitive demand) during performance of a lower
limb task is unknown.
Section 1: Cortical Contributions to Voluntary Contractions
Functional magnetic resonance imaging (fMRI) is a neuroimaging procedure that
measures brain activity by detecting changes in magnetization between oxygen-rich and
oxygen-poor blood in the brain that is the result of increases or decreases in neural
activity as areas of the brain becomes more or less active, respectively. Areas with
increased neural activity generate a blood-oxygen-level dependent (BOLD) contrast that
can present associated brain activation graphically in the form of a color-coded map that
indicates strength of activation across the brain or a particular region of interest (ROI).
Changes in BOLD signal contrast are characterized by the intensity percent signal change
(PSC, %) and volume of activation (µl) (Huettel et al., 2004).
Early fMRI studies were able to confirm the somatotopic organization of the
primary motor cortex by having participants perform voluntary movements of different
joints; the maps created confirmed that the lower limb is represented medially, the upper
limb is represented more laterally, and the hand occupies a large portion of the
somatotopic map of the primary motor cortex (M1) due to the precise fine motor
movements it performs (Lotze et al., 2000; Rao et al., 1995). fMRI studies have also
established the relationship between changes in brain activation and force production.
For example, activation in several motor areas of the cortex has been demonstrated
8
during submaximal and fatiguing handgrip tasks (Dai et al., 2001; Liu et al., 2003).
Further, during sustained, graded contractions of the hand and first dorsal interosseus
abduction, BOLD signal intensity in motor areas, such as the sensorimotor cortex,
premotor areas and cerebellum, have also been shown to increase linearly with increased
force production (Spraker et al., 2007; van Duinen et al., 2008).
Most studies evaluating cortical function during force production use the upper
limb; however, the relationship between cortical activation and force intensity in the
lower limb has not been determined. Differences exist in cortical activation patterns and
intensity between upper and lower limb movement (Luft et al., 2002; Miyai et al., 2001),
in part because the lower limb has: 1) a different somatotopic location, 2) reduced fine
motor control, 3) larger muscle mass to motor unit ratios (Feinstein et al., 1955), and 4)
fewer direct corticospinal connections compared with upper limb muscles (Brouwer &
Ashby, 1990).
Movement in the lower limb has been shown to correlate with changes in BOLD
signal intensity (cortical activation) in the primary motor cortex and sensory cortex (Huda
et al., 2008; Orr et al., 2008). Active and passive ankle dorsiflexion and plantarflexion
tasks also activated similar cortical regions (Ciccarelli et al., 2006; Dobkin et al., 2004),
and graded dorsiflexion movements of the right ankle have produced graded BOLD
signal changes in motor areas (MacIntosh et al., 2004); however, the relation between
cortical activation and force intensity has not been systematically determined.
The intensity, volume and location of cortical activation produced during force
production will vary based on many factors, including the amplitude of movement,
intensity of force exerted and complexity of the task required. For example, it has been
9
demonstrated that the basal ganglia plays a significant role in the control of force in hand
muscles (Kinoshita et al., 2000; Spraker et al., 2007) and BOLD signal activity can vary
substantially in the different nuclei of the basal ganglia according to the task selection
and prediction, and amplitude and rate of force development (Prodoehl et al., 2009;
Spraker et al., 2007). Further, anatomical regions associated with cortical activation
during force production and control can be anticipated based on previous evidence. See
Table 1.1 for examples of relevant anatomic regions of interest during static contractions
in the lower limb across multiple force levels.
10
Table 1.1. Regions of interest. ROI for control of lower limb function during static contractions across forces.
Cortical Region
Function
Primary Motor
 Production and control of voluntary movement; May specify muscle activation levels or
Cortex (M1)
could be transformed into muscle-specific signals in the spinal cord (Kalaska, 2009)
 May also adapt to the aging neuromuscular system to assist with production of muscle
strength (Plow et al., 2013)
Supplemental
 Contralateral M1 fibers converge with ipsilateral SMA (Luft et al., 2002)
Motor Area
 Helps with assembly of central motor programs, sequences simple movements and
(SMA)
execution of a motor sequence test (Roland et al., 1980).
Basal Ganglia
Does not initiate movement as previously thought, but:
 Contributes to automatic execution of movement sequences, adjust magnitude of globus
pallidus internal (GPi) inhibitory output to increase or decrease movement, and permits
desired movements and inhibit unwanted competing movements (Squire et al., 2013)
 Assists with task selection, prediction, and amplitude and rate of force development
(Prodoehl et al., 2009)
 Portions scale in activation intensity with increasing force amplitude with upper limb
(Grafton & Tunik, 2011; Spraker et al., 2007; Vaillancourt et al., 2004; Vaillancourt et
al., 2007)
 Prepares motor readiness state in response to danger, transforms emotional responses to
behavioral responses (Butler et al., 2007)
 May play a role in subcortical fight or flight programs (Marchand et al., 2009).
Cerebellum
 Adapts goal-directed arm movements (Tseng et al., 2007), coordinated movement, motor
learning, reflex adaptation (Glickstein, 2007), sensorimotor control (Manni & Petrosini,
2004; Stoodley & Schmahmann, 2009; Stoodley & Schmahmann, 2010), and has been
shown to scale with force in the hand (Keisker et al., 2009; Kuhtz-Buschbeck et al.,
2008)
 Control of agonist-antagonist activity and inhibiting co-contraction of antagonist
muscles (Mari et al., 2014)
 May influence cognitive and emotional function (O'Reilly et al., 2010; Stoodley &
Schmahmann, 2010).
Visual Cortex
 Classifies and interprets environment stimuli enabling appropriate responses to external
visual stimuli (Squire et al., 2013)
 Lingual gyrus may assist target recognition (Gron et al., 2000)
 Calcarine gyrus (V1) may assist with the central visual field and spatial attention
(Martínez et al., 1999)
 Ipsilateral parietal lobule may assist with oculomotor and attention processes and
contribute to appropriate motor output in response to visual sensory input (Clower et al.,
2001)
Prefrontal Cortex
 Synthesizes sensory information from internal (affect, memory, reward) and external
(PFC)
(sensory, cortical and subcortical motor systems) environment to produce goal-directed
movement; Assists with planning, decision-making, behavioral control, emotions,
moods, and working memory; Communicates with M1 through premotor area and basal
ganglia (Squire et al., 2013).
 Critical in executive function and top-down control of behavior (Miller & Cohen, 2001)
Anterior Cingulate  Associated with autonomic responses (greater in men than women) (Wong et al., 2007)
Cortex (ACC)
 Conflict and error monitoring and detection, response selection, attention control, as well
as emotions, mood, action, anticipation, working memory, visuospatial orientation, and
navigation of body in space (Torta & Cauda, 2011)
Right Inferior
 Visual perception (Ishai et al., 1999)
Temporal Gyrus
 Pattern recognition (Herath et al., 2001)
 Modulation of neural responses that have become motivationally significant (Mesulam,
1998)
Left Superior
 Key component in working memory and involved in coordinating responses to increased
Frontal Gyrus
executive demand in working memory (Boisgueheneuc et al., 2006)
11
Sex Differences in Cortical Activation during Voluntary Contractions
Sex differences in cortical activity have also been shown during performance of
cognitive tasks. For example, men demonstrated asymmetric prefrontal activity in
response to a difficult mental-math task, whereas women did not demonstrate correlated
activity in prefrontal cortex, but instead demonstrated increased activation in limbic
regions (Wang et al., 2007). Further, women have demonstrated greater amounts of
BOLD signal activity in the ipsilateral frontal and parietal lobes than men during visually
guided reaching tasks that required arm and eye movements in the same direction (Gorbet
& Sergio, 2007) with greater bilateral distribution (Gorbet et al., 2010); as well as greater
frontoparietal activity during a visuospatial navigation task (Gron et al., 2000) than men.
However, sex differences in brain activation patterns during lower-extremity motor tasks
are unknown. Various indirect measures of motor unit activity (EMG) and assessments
of voluntary activation (e.g. techniques of stimulation along the neuraxis to determine
neural drive to the muscle and to the motor cortex), indicate no sex difference in
activation output from the motor cortex (Hunter et al., 2006; Hunter & Enoka, 2001;
Keller et al., 2011).
Cortical Contributions to Changes in Motor Output
In order to execute a voluntary goal-directed motor task, the cerebral cortex
communicates with a desired muscle via the corticospinal tract. The corticospinal
neurons originate in the primary motor cortex (M1) and project their axons through the
midbrain and pons, decussate in the medulla to the opposite side of the spinal cord. The
majority of these neurons terminates in the dorsolateral ventral horn of the spinal cord
12
and communicates with interneurons or motoneurones (decussation of the corticospinal
axons will result in activation of the left motor cortex during performance of a right side
task and vice versa) (Squire et al., 2013). Motoneurones synapse on multiple muscle
fibers via neuromuscular junctions that convert the descending neural input into force
output forming a motor unit (a single motoneurone and all of the muscle fibers it
innervates (Sherrington, 1925). A single motoneurone will synapse with multiple muscle
fibers and a single muscle will be innervated by multiple motor units. Force generation
occurs via two strategies: recruitment of motor units (predominantly at lower forces) and
rate coding or optimal discharge frequency of the motor unit (predominantly at higher
forces) (De Luca et al., 1982a; Monster & Chan, 1977; Van Cutsem et al., 1997).
However, for the tibialis anterior muscle, motor unit recruitment is of greater significance
across the full range of contraction force intensity because of its large recruitment range
(up to ~90% MVC) (Van Cutsem et al., 1997) compared with intrinsic muscles of the
hand such as the first dorsal interosseus which has a lower recruitment range to ~50%
MVC (De Luca et al., 1982a). Thus, because the motor unit communicates with the motor
cortex, factors that influence motor cortex output (descending drive), motor unit
recruitment, or motor unit discharge rates will directly influence the motor output of that
muscle.
Section II: Contributions to Motor Output in the Lower Limb
Force Fluctuations during Voluntary Contractions
Sustained isometric contractions are important for a wide variety of functional,
vocational, and sporting tasks, particularly at submaximal levels (Grabiner & Enoka,
13
1995). In the upper limb, sustained submaximal contractions may be used to grip a pen
or hold a glass, or to perform a complex surgical procedure; sustained contractions in the
lower extremity may include accelerating or decelerating a vehicle, or walking over
various surfaces. Variability of force about an average value can be quantified in
absolute terms as the standard deviation (SD) or in relative terms as the coefficient of
variation (CV) (Enoka et al., 2003). The SD of force fluctuations characterizes the
magnitude of fluctuations in the system output and serves as an index of the average
deviation of scores in the distribution from the distribution mean; while the CV indicates
the scatter of responses relative to the mean response amplitude and illustrates the way
the system output changes over time independent of the magnitude of response (Enoka et
al., 2003). Typically, the standard deviation of force increases linearly with increased
force production (Figure 1.2) and is usually lowest at the lowest forces (Enoka et al.,
2003; Moritz et al., 2005; Taylor, A. M. et al., 2003; Tracy, 2007a). The variation in
force (CV) controls for force and is greatest at lower force levels, decreasing as
contraction intensity increases (Figure 1.2) (Galganski et al., 1993; Slifkin & Newell,
1999).
14
Figure 1.2. Standard Deviation and Coefficient of Variation of Force. Standard deviation (A)
and coefficient of variation (CV; B) for force during constant-force isometric trials performed at
2, 5, 10, and 50% MVC in young and old subjects. Values are means ± SE. *p < 0.05 between
age groups. From Tracy and Enoka (2002).
At low-to-moderate forces, force fluctuations appear to be the result of primarily
two factors that influence the output of motoneurones: synaptic noise and common drive
to the motoneurones (De Luca et al., 2009; Dideriksen et al., 2012; Hamilton et al.,
2004). In order to sustain a low target force during an isometric contraction, motor units
are recruited and the discharge rates modulated in unison through a centrally mediated
common drive (De Luca et al., 2009; De Luca et al., 1982b). This modulation of motor
15
unit discharge is characterized as a low-frequency oscillation of 2-3 Hz and is thought to
be a major contributor to the force fluctuations during isometric contractions at low to
high forces (Mottram et al., 2005; Negro et al., 2009). At very low forces the variability
in discharge rate is thought to be the primary contributor to force fluctuations
(Jesunathadas et al., 2012; Negro et al., 2009). Relative to muscles in the upper limb
(FDI, elbow flexors), the tibialis anterior has fewer synaptic inputs onto motoneurones
(Brouwer & Ashby, 1990) which decreases synaptic noise in the system; thus, at low
forces, motor unit discharge rate variability probably has its greatest influence on the CV
in upper limb muscles and lesser effects in the tibialis anterior (Jesunathadas et al., 2012).
Further, at very low force levels (< 5% MVC), descending input from the visual cortex
has also been demonstrated to influence the steadiness of sustained voluntary
contractions in the lower extremity (Tracy, 2007b). As force increases to moderate and
higher levels, the number of recruited motor units decreases (Fuglevand, A. J. et al.,
1993) and amplitude of force fluctuations decreases (Moritz et al., 2005). As a result,
force fluctuations at higher force levels are related to variability in descending drive from
the central nervous system, not synaptic noise or intrinsic motoneurone properties
(Dideriksen et al., 2012).
Sex Differences in Motor Output
The ability to sustain steady low and moderate force contractions can be
influenced by a variety of factors: age (Galganski et al., 1993; Laidlaw et al., 2000),
fatigue (De Luca et al., 1982b; Singh et al., 2010), arousal (Lorist et al., 2002; Noteboom,
Barnholt, et al., 2001), intensity and type of contraction (Cresswell & Loscher, 2000;
Griffith et al., 2010; Semmler et al., 2007), and muscle group (Hunter, Yoon, et al., 2008;
16
Jesunathadas et al., 2012; Tracy, Mehoudar, et al., 2007). It is unclear whether sex
differences contribute to decreased steadiness because in many studies, sex differences
are not assessed. When performing submaximal isometric contractions, women have
been reportedly more (Endo & Kawahara, 2011), less (Brown et al., 2010; Brown et al.,
2009; Grunte et al., 2009), or similarly (Baweja et al., 2009) steady when compared with
men. For example, women demonstrated greater overall force fluctuations than men
across multiple force levels (2.5, 5, 10, 25, 50 and 75% of maximal voluntary contraction
(MVC) force (Brown et al., 2010). In a target-matching task across similar target forces
(2.5, 5, 10, 30, 50 and 80% of MVC force), Tracy (2007a) found differences in force
fluctuations between ankle muscle groups (ankle dorsiflexion and plantarflexion), but
there were no sex differences between young or older adults. Given the lack of mention
in the literature and disparity of results, it remains unclear if women are less steady than
men when performing submaximal isometric contractions.
Mechanisms related to sex differences in steadiness are equally unclear. Women
have been reported to have lower motor unit discharge rates and higher discharge rate
variability than men during isometric elbow flexion contractions (Brown et al., 2009).
Both of these factors would result in a significant increase in force fluctuations when
compared with men (Moritz et al., 2005); however, there is very little additional evidence
to support this finding in the literature.
Age Differences in Motor Output
Greater force fluctuations with advanced age have also been observed across
various muscle groups and particularly at the lower intensity contractions (Enoka et al.,
2003; Tracy, Dinenno, et al., 2007; Tracy et al., 2005). With advanced age, the
17
motoneurone pool undergoes remodeling that results in decreased motor units numbers
and altered relations between discharge rates and recruitment thresholds (Barry et al.,
2007); the age difference in force fluctuations therefore appears to be due to age-related
changes in the inputs to the motoneurone pool (Barry et al., 2007) with possibly some
influence of motor unit discharge rate variability in older adults (Barry et al., 2007;
Kornatz et al., 2005; Laidlaw et al., 2000; Tracy et al., 2005). Age-related changes in
visual-motor processing may also contribute to altered motoneuronal inputs causing
increased force fluctuations during static contractions with age (Fox et al., 2013;
Henningsen et al., 1997; Seidler-Dobrin & Stelmach, 1998; Tracy, Dinenno, et al., 2007).
Influence of Muscle Fatigue on Motor Output
As an isometric contraction is sustained, motor output becomes progressively
more variable, the muscle begins to fatigue, and descending cortical drive increases in an
effort to sustain the required force (Missenard et al., 2009; Riley et al., 2008). Muscle
fatigue is defined as an exercise-induced reduction in muscle force or power caused by
impairments within the neuromuscular system that is reversible with rest (Enoka &
Duchateau, 2008; Fitts, 1994). It begins at the onset of exercise, progresses during
exercise and starts to recover once exercise stops. The magnitude of muscle fatigue
caused by exercise, such as a target-matching task, can be quantified as the decline in the
maximal force or power measured immediately after the fatiguing contraction (Hunter,
Critchlow, Shin, et al., 2004; Taylor, J. L. et al., 1996). If the contraction is sustained it
will eventually result in task failure (Gandevia, 2001). As fatigability in a muscle
increases, electromyography (EMG) activity will also increase progressively throughout
18
the contraction as the already active fibers lose force and more motor units are recruited
(Enoka & Duchateau, 2008; Hunter, Duchateau, et al., 2004) (Figure 1.3).
Figure 1.3. Characteristics of a Force Task Performed to Failure. Representative EMG data
for a force tasks as performed by one individual using the first dorsal interosseus muscle. The
data comprise the EMG for the antagonist (second palmar interosseus), agonist (first dorsal
interosseus) muscles, and the abduction force. EMG and force fluctuations increase as
fatigability increases (Enoka & Duchateau, 2008).
Mechanisms of muscle fatigue are task dependent and will vary based on sex,
muscle group, task, and age (Enoka & Duchateau, 2008). For example, when participants
performed a sustained isometric contraction of the elbow flexor muscles at 20% and 80%
MVC to task failure, men demonstrated a shorter time to task failure than women during
the 20% MVC task, but similar times to failure for the 80% MVC task (Yoon et al.,
2007). When performing sustained isometric contractions with the elbow flexor muscles
and ankle dorsiflexor muscles, participants demonstrated longer times to task failure
when performing a force task compared to the position task, and there were no sex
differences (Hunter et al., 2002; Hunter, Yoon, et al., 2008). Hunter, et al (2005) found
that time to task failure was shorter for a position task compared to a force task for older
adults when compared with young adults for elbow flexor muscles (Hunter et al., 2005).
However, there was no difference in time to task failure between loads when similar tasks
were performed in the lower extremity (Griffith et al., 2010).
19
Similar central mechanisms that contribute to increased force fluctuations will
also contribute to muscle fatigue, including suboptimal activation of the motor cortex
(Gandevia et al., 1996), decreased neural drive or excitatory input to the motoneurone
(Taylor, J. L. & Gandevia, 2008), and decreased responsiveness in motoneurone
activation (Herbert & Gandevia, 1999). As a muscle progressively fatigues, descending
drive increases, as evidenced by increased EMG and ratings of perceived exertion (Riley
et al., 2008), and progressively declines in voluntary activation (level of voluntary drive
to the muscle) (Gandevia, 2001). Although various measures of motor unit activity
(EMG) and assessments of voluntary activation (e.g. techniques of stimulation along the
neuraxis to determine neural drive to the muscle and to the motor cortex), indicate no sex
difference in motor cortex output activation (Hunter et al., 2006; Hunter & Enoka, 2001;
Keller et al., 2011) women may be more sensitive to changes in common drive than men.
This is plausible because women demonstrate differences in motor unit discharge rates
and discharge rate variability compared with men at similar intensity contractions (Brown
et al., 2009), however, this relationship requires has not been previously investigated.
Influence of Cognitive Demand on Motor Output
Imposition of increased cognitive demand can increase descending drive,
particularly when cognitive demand in increased simultaneously with a sustained
isometric contraction, a dual-task. The influence of dual tasks has been shown previously
in the literature using multiple combinations of tasks. In young adults, for example,
cognitive performance declines and force fluctuations increase when a cognitive task was
imposed (reaction time) during sustained isometric tasks with hand muscles (Lorist et al.,
2002; Zijdewind et al., 2006); however, force fluctuations were affected more during the
20
isometric fatiguing contractions than during a 5% submaximal non-fatiguing contraction
(Lorist et al., 2002). Further, steadiness of the elbow flexor muscles declined (increased
force fluctuations) and time to failure of a sustained 20% MVC submaximal task was
reduced (increased fatigability) in young adults when simultaneously performing a
demanding cognitive task that increased anxiety (counting backwards by 13) (KellerRoss, Pruse, et al., 2014; Yoon et al., 2009). Individuals who were weaker (primarily
women) show the largest decrement in time to task failure when the stressful cognitive
task was imposed during the fatiguing contraction (Keller-Ross, Pruse, et al., 2014; Yoon
et al., 2009). However, it is not known how changes in cognitive demand in the presence
of various force levels would influence steadiness in men and women.
Decrements in dual-task performance are also exacerbated with age (Springer et
al., 2006). For example, when older and young adults performed an n-back task and a
force-tracking task, older and young adults performed each task in isolation similarly;
however, during simultaneous performance, older adults demonstrated significantly
greater losses in force control than young (Voelcker-Rehage et al., 2006). Older adults
are typically weaker than young adults, with older women being weaker than older men
for upper and lower limb muscles (Galganski et al., 1993; Laidlaw et al., 2000; Tracy &
Enoka, 2002), possibly increasing susceptibility to increased fatigability when a cognitive
task is imposed. However, it is not known whether fatigability with increased cognitive
demand is exacerbated with advanced age. Furthermore, the effects of increased
cognitive demand on lower limb fatigability in young or older adults are not known.
Given that fatigue and increased arousal increase descending drive from the central
nervous system (Missenard et al., 2009; Noteboom, Fleshner, et al., 2001), potentially
21
further increasing force fluctuations, and that mechanisms that modulate steadiness occur
in different areas of the central nervous system, we utilized both fatigue and increased
cognitive demand to influence descending drive to determine differences in steadiness
between men and women. Therefore, aim 2 of this dissertation examines the influence of
cognitive demand on fatigability and steadiness of low- to moderate-force isometric
contractions of the lower extremity in young and old men and women.
Dual-Tasks
Information from multiple areas of the cortex can influence motor output. For
example, the primary motor cortex (M1, where production and control of voluntary
movements occur) receives information from the cerebellum (which coordinates
movement), while the supplemental motor area (responsible for postural stabilization,
sequencing of events) will receive input from the basal ganglia (which regulates
inhibitory output to regulate movement) (Squire et al., 2013). Further, input from the
prefrontal cortex, which receives and synthesizes input from the major sensory systems,
basal ganglia and limbic system, provides information to the motor cortex via the
premotor cortex to assist with planning, decision-making, and executive function tasks
(Squire et al., 2013; Takahara et al., 2012). Executive function (which includes volition,
planning, purposive action, and action monitoring), anxiety, and stress are modulated in
prefrontal cortical regions and the anterior cingulate cortex (Banich et al., 2009; Miller,
2000; Owen et al., 2005; Schweizer et al., 2013). Changes in performance for older
adults in dual-tasks appear to be especially sensitive to cognitive tasks that require
executive function (Yogev-Seligmann et al., 2008). Furthermore, increased
monoaminergic drive and neuromodulatory inputs potentially increase motor unit
22
discharge rate variability and therefore force fluctuations at very low forces. If a
cognitive demand task is perceived to be more of a stressor for the older adults than the
young, motor output may become more variable and steadiness may decrease. Thus,
there are many different pathways through which the neural connections from areas
associated with cognition and anxiety, particularly the cingulate and prefrontal cortices,
could directly alter motor function.
The sensorimotor cortex provides information to the motor cortex based on
stimuli it has interpreted from the environment. Bottom-up processing occurs if the
sensory information is perceived to be novel or salient, thus requiring attention or a
response. If, however, it is necessary to ignore irrelevant distractions by enhancing or
inhibiting neural activity that will ensure successful task completion through premotor
cortex modulation , top-down modulation has occurred (Gazzaley & D'Esposito, 2007;
Gazzaley & Nobre, 2012).
The effort of sustaining attention on a task is affected by fatigue and stress
(McDowd, 2007) and can lead to diminished motor and cognitive performance,
particularly when the motor and cognitive task are performed simultaneously, or as a
dual-task. For example, when performing an auditory choice reaction task and fatiguing
submaximal finger abduction simultaneously, increased motor fatigue resulted in a
drastic deterioration in the choice reaction task performance and increased force
variability compared to when the tasks were performed in isolation (Lorist et al., 2002).
Capacity theories of attention assume that there are attentional resource limitations on the
ability to perform multiple tasks simultaneously (Hiraga et al., 2009; Kahneman, 1973;
McDowd, 2007) and that attempting to perform two tasks simultaneously will result in
23
diminished performance in either one or both of the tasks (Tombu & Jolicoeur, 2003).
Thus, attentional limitations during performance of dual-tasks have been demonstrated in
both young and older adults and the capacity theory of attention may provide an
explanation for decrements in performance found in young and older adults under dualtask conditions. Changes in cognitive demand and stress (Christou et al., 2004; Yoon et
al., 2009) have also resulted in increased motor output variability in the upper extremity;
however, the influence of changes in cognitive demand and age on motor output to the
lower extremity is not known.
The central theme of this dissertation is to answer questions regarding the
influence of cortical input on force output during isometric contractions of the lower
limb. The purpose of aim 1 was to determine the force-related changes in cortical regions
across a range of high and low force intensities in the BOLD signal (intensity and
volume) during sustained isometric ankle dorsiflexion contractions in young men and
women. Thus, study 1, aim 1 in this dissertation determined the cortical regions involved
in force-related changes between low and high forces and those areas of the cortex that
modulate steadiness (force fluctuations) using fMRI during sustained isometric ankle
dorsiflexion contractions in young men and women. Aim 2 was to examine the influence
cognitive demand on fatigability and steadiness of low- to moderate-force isometric
contractions in young and old men and women. To address aim 2, two different studies
were conducted. Study 2, aim 2 exposed the role and importance of the cortical centers
during lower limb static contractions, this dissertation used varying levels of cognitive
demand and determined the change in motor output (steadiness and fatigue) in young
men and women. These experiments were also performed in older men and women
24
(study 3, aim 2) and compared with young adults to determine if age-related changes in
cortical centers would further disrupt motor output during the static contraction with the
leg muscles.
25
DISSERTATION AIMS AND HYPOTHESES
Central Question:
What is the influence of cortical input on force output during isometric contractions in
the lower extremity?
Central Hypothesis:
Force output of the lower extremity muscles is controlled by cortical inputs that vary with
the intensity of contraction, sex, age and cognitive demand.
Specific Aims
Aim 1: To determine the force-related changes in cortical regions across a range of
high and low force intensities in the BOLD signal (intensity and volume) during
sustained isometric ankle dorsiflexion contractions in young men and women.
Sub aims:
1. To determine if intensity and volume of activation in the motor areas of the brain
demonstrates a linear increase in activity as with increased force intensity.
Hypothesis: Motor areas of the brain will scale linearly in intensity and volume
with increased force intensity of the ankle dorsiflexor muscles.
2. To identify cortical areas that correlate with the modulation of force fluctuations
across a range contraction intensities. Hypothesis: At lower intensities of
contraction, larger fluctuations in force will be associated with greater activation
of cortical motor areas.
3. To determine whether there were any sex differences that could be detected in
brain activity and modulation of the low- and high-force isometric contractions
26
with the lower limb. Hypothesis: Men and women will demonstrate similar
changes in brain activity during static contractions of the lower extremity.
Aim 2: To examine the influence cognitive demand on fatigability and steadiness of
low- to moderate-force isometric contractions in young and old men and women.
Sub aims:
1. To compare both fatigability and amplitude of force fluctuations for a low- to
moderate-force isometric contractions in the presence and absence of varying
levels of cognitive demand in men and women. Hypotheses: Women will show
greater fatigability and fluctuations in force during the low-force fatiguing
contraction than men in the presence increased cognitive demand (a difficult
mental-math task).
2. To compare both the amplitude of force fluctuations and fatigability for low- to
moderate-force isometric contractions in the presence and absence of varying
levels of cognitive demand in young and old adults. Hypothesis: Old adults will
show greater reductions in time to task failure and greater fluctuations in force
than young as cognitive demand increased.
3. To compare variability of motor performance (steadiness and time to task failure)
between and within young and old adults with increased cognitive demand.
Hypothesis: Old adults will exhibit both greater between- and within-subject
variability in motor performance as cognitive demand increased.
27
CHAPTER II
Brain areas associated with force steadiness and intensity during
isometric ankle dorsiflexion in men and women
SUMMARY
Although maintenance of steady contractions is required for many daily tasks,
there is little understanding of brain areas that modulate lower limb force accuracy.
Functional magnetic resonance imaging (fMRI) was used to determine brain areas
associated with steadiness and force during static (isometric) lower limb target-matching
contractions at low and high intensities. Fourteen young adults (6 men, 8 women; 27.1 ±
9.1 years) performed three sets of 16 s isometric contractions with the ankle dorsiflexor
muscles at 10, 30, 50 and 70% of maximal voluntary contraction (MVC). Percent signal
changes (PSC, %) of the blood oxygenation level dependent (BOLD) response were
extracted for each contraction using region of interest (ROI) analysis. Mean PSC
increased with contraction intensity in the contralateral primary motor area (M1),
supplementary motor area (SMA), putamen, pallidum cingulate cortex, and ipsilateral
cerebellum (p<0.05). The amplitude of force fluctuations (SD) increased from 10 to 70%
MVC but relative to the mean force (coefficient of variation, CV %) was greatest at 10%
MVC. The CV of force was associated with PSC in the ipsilateral parietal lobule (r = 0.28), putamen (r = -0.29), insula (r = -0.33), and contralateral superior frontal gyrus (r =
-0.33, p<0.05). There were minimal sex differences in brain activation across the
isometric motor tasks indicating men and women were similarly motivated and able to
28
activate cortical motor centers during static tasks. Control of steady lower limb
contractions involves cortical and subcortical motor areas in both men and women and
provides insight into key areas for potential cortical plasticity with impaired or enhanced
leg function.
INTRODUCTION
Steady postural contractions that stabilize a limb with accuracy are required for
successful performance of many daily tasks including carrying objects, driving and
walking. Stabilizing contractions across a range of force intensities requires appropriate
neural activity in cortical centers. Early work in monkeys established the central role of
discharge rates of primary motor cortex (M1) cells during graded voluntary movements
of distal upper limb muscles (Evarts, 1968). Subsequent development of imaging
techniques in humans including functional magnetic resonance imaging (fMRI) have
established positive associations between contraction force intensity of the upper limb
and cortical activation indicated by the blood oxygenation level dependent (BOLD)
response [e.g. (Keisker et al., 2009; Spraker et al., 2007; Thickbroom et al., 1998; van
Duinen et al., 2008)]. However, there remains uncertainty as to the role of intensity and
volume of cortical activation in force regulation (Dai et al., 2001) and the motor areas
involved with increased activation especially among lower-to-moderate force
contractions, which are forces that many daily tasks are performed (Keisker et al., 2009;
Spraker et al., 2007; van Duinen et al., 2008).
Despite the primary role of lower limb muscles as agonists and stabilizers during
functional tasks such as driving and walking, most studies have studied upper limb
muscles and little is understood about cortical activation during leg movements.
29
Understanding key cortical areas associated with control of the lower leg in healthy
young adults establishes a foundation for identifying the plasticity of these areas with
impaired motor function that can occur with aging and neurological conditions as well as
enhanced function that is possible with physical exercise in all populations. Some studies
have determined areas of activation during foot exercise [e.g. (Ciccarelli et al., 2005;
Huda et al., 2008; MacIntosh et al., 2004)] but the relation between activation and force
intensity has not been systematically determined. For lower limb muscles however,
activation strategies within the cortex (shown as the intensity of the BOLD signal and
volume of activation) may differ to that of the more commonly studied hand muscles: the
lower limb muscles have a reduced need for fine motor control compared with hand
muscles, they have a the larger muscle mass and motor unit ratio (Feinstein et al., 1955),
and fewer direct corticospinal connections (Brouwer & Ashby, 1990) compared with
upper limb muscles. Further, the tibialis anterior has a greater motor unit recruitment
range than for example, the first dorsal interosseus (De Luca et al., 1982a; Moritz et al.,
2005; Van Cutsem et al., 1997). Therefore, a purpose of this study was to determine
force-related changes across a range of high and low forces in the BOLD signal (intensity
and volume) of cortical regions during isometric ankle dorsiflexion contractions in
healthy young adults. Because the rate of movement during dynamic or repetitive short
isometric contraction mode can affect the BOLD signal (Rao et al., 1996), we used
sustained isometric contractions between 10% and 70% of maximal strength. We
hypothesized that motor areas of the brain would scale linearly with an increase in force
intensity of the ankle dorsiflexor muscles.
30
When sustaining a postural static contraction as is often required during standing,
the force exerted to maintain the isometric contraction fluctuates around a mean target
force and is often referred to as steadiness (Enoka et al., 2003). The amplitude of the
force fluctuations (standard deviation (SD) of the force) increases with force intensity due
to activation of more motor units. When the standard deviation is normalized to the mean
force (coefficient of variation, CV), the force fluctuations do not increase with intensity
and are usually larger at lower intensities of contraction [e.g. (Jesunathadas et al., 2012;
Moritz et al., 2005; Taylor, A. M. et al., 2003; Tracy, Mehoudar, et al., 2007). CV of
force is mostly mediated by low-frequency oscillations in neural drive (< 2-3 Hz) seen as
the oscillations of the trains of motor unit action potentials (Dideriksen et al., 2012;
Negro et al., 2009), with a greater influence of synaptic noise and motor unit discharge
rate variability at low forces (Jesunathadas et al., 2012). The low-frequency oscillating
neural drive reflects an integration of both descending and afferent inputs (Dideriksen et
al., 2012; Farina et al., 2012; Negro et al., 2009). A novel aspect of this study was to
identify and understand those cortical areas that are related to the force fluctuations
across a range of forces in young adults. We hypothesized that at lower intensities of
contraction, larger fluctuations in force (CV) would be associated with greater activation
of cortical motor areas.
Both men and women were tested, so we also determined whether there were any
sex differences that could be detected in brain activity and modulation of the low- and
high-force isometric contractions with the lower limb. Sex differences have been shown
in brain activity during cognitive tasks of equal performance in men and women, such
that women have greater brain activation for the same task (Wang et al., 2007) and some
31
differences in motor control during finger tapping (Lissek et al., 2007). Whether there
are sex differences in brain activation during motor tasks with the lower limb has not
been assessed. Various indirect measures of motor unit activity (electromyography,
EMG) and assessments of voluntary activation (e.g. techniques of stimulation along the
neuraxis to determine neural drive to the muscle and to the motor cortex), indicate no sex
difference in the output of activation from the motor cortex (Hunter et al., 2006; Hunter
& Enoka, 2001; Keller et al., 2011). We hypothesized therefore, that men and women
would have similar PSC during graded and controlled static contractions.
METHODS AND MATERIALS
Fourteen healthy young adults (6 men and 8 women; mean ± SD; 27.1 ± 9.1
years, 170.5 ± 9.5 cm in height, 66.5 ± 11.2 kg in body mass) volunteered to participate
in the study. All participants were healthy with no known neurological or cardiovascular
diseases and were naive to the protocol. Each participant provided informed consent, and
the protocol was approved by the institutional review boards at Marquette University and
Medical College of Wisconsin. Participants reported to the laboratory once for a
familiarization session and then for an experimental session in the MRI scanner.
Set Up and Mechanical Recordings. Participants laid supine in a 3.0 Tesla short
bore MRI Scanner (General Electric Healthcare, Waukesha, WI) with the hip and knee at
45° of flexion (full extension is 0°). The right foot was assessed with the ankle in a
neutral position (0° dorsiflexion). During the static (isometric) voluntary contractions,
force of the ankle dorsiflexor muscles was measured with a force transducer (Transducer
Techniques, Temecula, CA) mounted at right angles under a footplate that was adjustable
for angle and was rigidly secured to the distal end of the scanner bed (polycarbonate
32
platform). The forefoot was secured to the footplate via a strap placed 1-2 cm proximal
to the metatarsophalangeal joint of the toes. The forces detected by the transducer were
recorded online at 500 samples/s with a Power 1401 A/D converter and Spike2 software
(Cambridge Electronics Design, Cambridge, UK). The force signal was displayed via a
rear-projection visual display system for participant feedback. During the submaximal
contraction the visual display feedback was adjusted for each participant using their
maximal strength value. A horizontal cursor representing the baseline was displayed at
12.5% of the height of the screen and a horizontal cursor representing the required target
force was displayed at 75% of the height of the screen. The force output was displayed at
a refresh rate of 60 Hz and a resolution of 800 × 600 pixels. Each participant was asked
to trace the horizontal cursor with the force signal as steadily and accurately as possible.
Experimental Protocol. Once the participant was setup in fMRI environment,
they performed at least three maximal voluntary contractions (MVC) for 3-5 s duration to
obtain maximal strength (Figure 2.1). If peak forces from two of the three trials were not
within 5% of each other, additional trials were performed until this was accomplished.
The greatest force achieved by the subject was taken as the MVC and used as the
reference to calculate the target force. Each participant performed three sets (runs) of
isometric contractions at 10, 30, 50 and 70% of MVC in a randomized order. Each
contraction was held for 16 s followed by 60 s rest to avoid fatigue. Participants received
real-time force feedback during the contraction via a rear projection visual display system
and were required to track a horizontal target line on the display screen.
33
Figure 2.1. Experimental protocol. Three maximal voluntary contractions (MVC) of the ankle
dorsiflexor muscles were performed to determine the target torque. Each subject performed three
sets (runs) of isometric contractions at 10, 30, 50 and 70% of MVC in a randomized order. Each
contraction was held for 16 s followed by 60 s rest to avoid fatigue. Participants received realtime torque feedback during the contraction via a rear projection visual display system and were
required to track a horizontal target line on the display screen.
MRI Acquisition. Magnetic resonance images were collected in General Electric
Signa Excite 3.0 Tesla short bore MR Scanner (GE Healthcare, Waukesha, WI).
Functional MRI was used to quantify the blood oxygenation level dependent (BOLD)
contrast (T2* weighted imaging) overlaid on a T1 weighted anatomical image for each
subject. An 8-channel array Radio Frequency receive head coil (GE Healthcare,
Waukesha, WI) was used to obtain 36 sagittal plane slices (thickness = 4 mm) across the
entire brain volume using an echo-planar imaging sequence (64 × 64 matrix, 240 × 240
mm2 field of view, TE = 25 ms, TR = 2000 ms, and flip angle = 77°). Voxel were 3.75 ×
3.75 × 4 mm. Immediately after completion of the protocol, 148 high resolution spoiled
GRASS (gradient-recalled at steady state) anatomical images (thickness = 1 mm) were
collected (256 × 244 matrix, TE = 3.9 ms, TR = 9.5 ms, and flip angle = 12°).
34
Data Analysis
Mechanical Data Analysis: The torque for each MVC and submaximal
contractions was calculated as the product of force and the distance between the ankle
joint and the point at which the foot attached to the middle of strap of the force
transducer. The MVC torque was quantified as the average value over a 0.5s interval that
was centered about the peak. The fluctuations in torque for each 16 s contraction were
quantified in two ways: (1) the standard deviation (SD) of the torque, and (2) the
coefficient of variation (CV = SD/mean × 100%) which normalized the absolute
amplitude of the fluctuations to the mean torque exerted during each contraction.
fMRI Data Analysis: The public domain software, Analysis of Functional
NeuroImages (AFNI, http://afni.nimh.hih.gov/afni/) was used to analyze the fMRI data
sets. For each participant, all acquired functional 2-D images from the scanner were
converted to AFNI format and were aligned with slice timing correction. The time series
of functional image volumes were spatially realigned to correct the effect of head motion.
General linear modeling [3dDeconolve, AFNI (Cox, 1996)] was used to regress a model
of the contraction and rest blocks with the BOLD data. All contraction blocks (total 12
contractions = 4 intensities × 3 trials) were used for the model to calculate regression
coefficient of each voxel, and then used to create task-related activation map. In
addition, 3 contraction blocks at each intensity condition were also used for the model to
create a task-related activation map at each contraction intensity separately (Figure 2.1).
Separate amplitudes were computed for each of the four contraction intensities. These
amplitudes were warped to Talairach (Talairach & Tournoux 1988) space and blurred (4
mm FWHM Gaussian), then were used as the percent signal change (PSC, %). The
35
group analysis was performed for contraction versus rest (3dttest). The results were
thresholded using AlphaSim (whole brain corrected p = 0.05, cluster size = 168 ul) to
create a task-related activation map. This map was used as a mask for regions of interest
(ROI) analysis later. The percent signal change was extracted for each contraction
intensity for each subject. Repeated measures ANOVAs were performed to test for
differences across force levels (10, 30, 50 and 70% of MVC: force effect) in the BOLD
PSC of each activated area.
To compare the activation volume in each anatomical area separately within the
whole brain, we used the CA N27 macro level map in the AFNI toolbox as a mask rather
than a task-related activation map. A total of 116 region of interest (ROI) masks were
overlapped with PSC amplitude data for the 4 different torque intensities. Data were
thresholded with the average value of all PSC calculated from ROI analysis. The volume
in each area that showed a higher signal change than the threshold was used as the
activation volume. Repeated measures ANOVAs were performed to test the force effect
on the activation volume of each 116 regions. One participant was excluded from the
study due to severe motion artifact (> 2 mm).
Statistical Analysis. Data are reported as means  SD within the text, and
displayed as means  SE in the figures. Three-way ANOVAs (Sex as a fixed factor) with
repeated measures on individual run (1, 2, and 3) and force levels (10, 30, 50, and 70% of
MVC) were used to test for between- and within-group differences in average torque and
torque fluctuations. Two-way ANOVAs (Sex as a fixed factor) with repeated measures
on intensity of torque (10, 30, 50, and 70% of MVC) were used to test for between- and
within-group differences in percent signal change and activation volume. When the
36
significant main effect of the force level was found, a contrast option was used to
compare between each intensity of torque (10, 30, 50, and 70% of MVC) and the trends
(linear, quadratic and cubic) were also determined using the tests of within-subjects
contrasts table. When a significant sex-related interaction and main effect of sex were
found, preplanned t-tests were performed at each intensity to find the sex difference.
Pearson product-moment correlations were used to determine the relationships between
the force fluctuation and the PSCs of BOLD response in a selected area. We further
examined trends in the percent signal change with increased intensity of force (e.g.
logarithmic and sigmoidal trends) of using the curve estimation function in SPSS
program as others have done (Ashe, 1997; Cheney & Fetz, 1980; Dettmers et al., 1996).
A significance level of p < 0.05 was used to identify statistical significance.
RESULTS
Head Movement
Maximal head displacements were 0.54 ± 0.5 mm, 0.27 ± 0.1 mm and 1.37 ± 1.25
mm for anterior-posterior, right-left, inferior-superior directions, respectively. One
participant was excluded due to excessive head motion.
Torque (Force) and Steadiness
Torque: MVC torque of the ankle dorsiflexors was 31.4 ± 6.8 Nm (range 21.0 Nm to 43.9
Nm). For the submaximal target contractions, there was no difference in torque between
the three runs [F(2, 26) = 0.49, p = 0.62] so the three runs were averaged for each
intensity of torque (Figure 2.2A). Average torque was significantly different between
each target torque intensity [main effect of force, F(3, 10) = 81.4, p < 0.001]. Men were
37
stronger than women [men vs. women; 35.7 ± 6.2 vs. 28.2 ± 6.1 Nm, t(12) = 2.29, p =
0.041), but when normalized to the absolute maximal strength (MVC) they performed the
contractions at similar intensities.
38
Figure 2.2. Torque and steadiness. (A).
Average torque output at 10, 30, 50, and
70% of MVC of the ankle dorsiflexor
muscles. Most subjects traced the target
line so that the standard errors were
relatively small (ranged 0.011 to 0.102 for
10% and 70% conditions respectively)
compared with the mean force output.
Average torque was significantly different
between each target torque level (p <
0.001); (B). Standard deviation (SD) of
torque in men (closed circle) and women
(open circle). The SD of torque increased
as intensity of contraction increased from
10 to 70% of MVC (p < 0.001); (C).
Coefficient of variation (CV) of torque in
men (closed circle) and women (open
circle). CV at the 10% condition was
significantly greater than the other
conditions (p < 0.001). The CV decreased
and was described as a quadratic trend (p
< 0.001). There was no difference in
torque, SD of torque, and CV of torque
between the three runs (p > 0.05) and so
all run data were averaged across each
torque level. Shown are the means (±
SEM).
39
Torque Fluctuations (Steadiness): The SD of torque did not differ between the three runs
[F(2, 26) = 1.65, p = 0.21] so SD from the three runs were averaged for each intensity of
torque. The SD of torque increased with intensity of contraction from 10 to 70% of MVC
[main effect of torque intensity, F(3, 11) = 42.0, p < 0.001] in a linear [F(1, 13) = 90.4,
p < 0.001] and quadratic trend [F(1, 13) = 12.6, p = 0.004] (Figure 2.2B). There was no
difference SD of torques between men and women [sex effect, F(1, 12) = 0.52, p = 0.48]
across contraction intensities [sex × intensity interaction, F(3, 36) = 0.03, p = 0.99].
To determine the CV, the amplitude of torque fluctuations (SD) was normalized
to the mean torque exerted for each contraction intensity, for each participant. There was
no difference in the CV (%) between the runs [F(2, 12) = 0.83, p = 0.46] so the CV
values from the three runs were averaged for each intensity of torque (Figure 2.2C). CV
of torque differed between intensities [main effect of torque intensity, F(3, 11) = 32.7, p
< 0.001] because the CV at the 10% MVC was greater than the other forces and with
difference between 30% and 70% MVC. Thus, the CV decreased with intensity of force
and best described as a quadratic trend [F(1, 13) = 83.0, p < 0.001] (Figure 2.2C). There
was no difference CV of torques between men and women [sex effect, F(1, 12) = 1.37, p
= 0.27] across contraction intensities [sex × intensity interaction, F(3, 10) = 0.36, p =
0.78].
Brain Activation Areas
ROI were generated and indicated that both cortical and subcortical regions were
significantly activated during right ankle isometric dorsiflexion (See Table 2.1 and Figure
2.3).
40
Table 2.1. Brain activation areas. Areas of brain activation during right ankle isometric dorsiflexion (n = 6
men and 8 women).
Coordinates
Contraction vs.
Cluster
(mm)
rest
Brain Regions
Size
(µl)
x
y
z
t-score z-score
L. Putamen
3247
27
4
13
6.52
4.35
L. Lingual Gyrus
1219
14
L. Calcarine Gyrus
1157
4
67
-6
6.15
4.21
67
12
5.9
4.12
R. Cerebellum (III)
1138
-12
35
-22
6.36
4.29
L. Paracentral Lobule (M1) (5mm from L. SMA)
800
4
15
56
6.04
4.17
L. Su. Occipital Gyrus
R. I. Parietal Lobule
582
11
85
4
5.92
4.12
542
-33
44
45
6.07
4.18
R. Putamen
484
-30
0
11
5.77
4.06
L. Su. Frontal Gyrus
368
22
13
53
6.07
4.18
R. I. Frontal Gyrus
312
-49
-4
17
5.95
4.14
L. I. Parietal Lobule
312
29
46
49
5.79
4.07
L. Su. Occipital Gyrus
281
17
86
20
5.75
4.05
R. Insula Lobe (Or R.I.F. G=Opercularis)
244
-41
-11
7
6.17
4.22
R. Insula Lobe
236
-39
13
15
6.31
4.27
L. Middle Cingulate Cortex
232
6
4
44
5.99
4.15
L. Pallidum (3mm for putamen; 5 for Thalamus)
205
20
5
1
6.66
4.4
R. I. Temporal Gyrus
195
-39
60
-5
5.8
4.07
R: Right; L: Light; Su: Superior; I: Inferior; Regions are listed accordingly to their cluster size. Coordinates
are used in mm in T-T Atlas space. The activation regions were thresholded with p < 0.0001 and an
activation cluster minimum of 168.8 µl so that all included voxels had a t-value > 5.32.
41
Figure 2.3. Brain Activation Areas during Right Ankle Isometric Dorsiflexion. Activation
areas were identified from contrasts between each contraction and rest. Cortical and subcortical
regions activated during each contraction intensity were displayed in orange to yellow colors (t =
6 to 10) on same slice, i.e. on axial, sagittal, and coronal plane (76, 74, 112). The activation
regions were thresholded with p < 0.0001 for each voxel and an activation cluster minimum of
168.8 μl so that all included voxels had a t-value > 5.32.
42
ROI Analysis using Activation Map
Percent Signal Change (Intensity of BOLD Response) and Torque
Left Putamen: There was a main effect of torque intensity for the mean PSC [F(3, 39) =
3.38, p = 0.028]. The PSC increased linearly with greater contraction intensity [F(1, 13) =
5.89, p = 0.030] so that the PSC of the 70% MVC was greater than the 10% MVC (p =
0.036). The PSC also showed logarithmic and sigmoidal increases as the torque increases
(logarithmic and sigmoidal; p = 0.041 and 0.048, respectively; Figure 2.4A).
Right Cerebellum (III): There was a main effect of torque intensity for the PSC [F(3,
11) = 4.56, p = 0.026]. The PSC increased linearly as contraction intensity increased
[F(1, 13) = 12.4, p = 0.004] so that PSC for the 50% and 70% MVC were greater than
the 10% MVC (p = 0.032 and p = 0.003, respectively). The PSC also showed logarithmic
and sigmoidal increases as the torque increases (logarithmic and sigmoidal; p = 0.001 and
p < 0.001, respectively; Figure 2.4B).
Left paracentral lobule/SMA: There was a main effect of torque intensity for the mean
PSC [F(3, 39) = 6.28, p = 0.001]. PSC increased linearly with the increase in contraction
intensity [F(1, 13) = 9.08, p = 0.010] so that the PSC for the 50% and 70% MVC were
greater than for the 10% MVC (p = 0.022 and p = 0.011, respectively). The PSC also
showed logarithmic and sigmoidal increases as the torque increases (logarithmic and
sigmoidal; p = 0.045 and 0.037, respectively; Figure 2.4C).
Left middle Cingulate Cortex: There was a main effect of torque intensity for the PSC
[F(3, 39) = 4.34, p = 0.010]. PSC increased linearly with increased contraction intensity
[F(1, 13) = 6.43, p = 0.025] so that the PSC for the 70% MVC was greater than for the
43
10% and 30% MVC (p = 0.040, 0.013 respectively). The PSC also showed logarithmic
and sigmoidal increases as the torque increases (logarithmic and sigmoidal; p = 0.018 and
0.011, respectively; Figure 2.4D).
Left Pallidum: There was a main effect of torque intensity for the mean PSC [F(3, 11) =
8.00, p < 0.001]. The PSC increased linearly with increased contraction intensity [F(1,
13) = 34.4, p < 0.001] so that the PSC for the 70% MVC was greater than all the other
conditions (p = 0.001 to 0.035). PSC for the 50% MVC was also greater than during the
10% MVC (p = 0.049). The PSC also showed logarithmic and sigmoidal increases as the
torque increases (logarithmic and sigmoidal; p = 0.002 and 0.008, respectively; Figure
2.4E).
44
Figure 2.4. Percent signal change in Regions of Interest. Percent signal change of regions of
interest (ROI) that scaled with torque intensity. Shown are group mean (± SEM) of percent signal
change during 10, 30, 50, and 70% of MVC torque conditions. (A). Left putamen (effect of
torque, linearity respectively; p = 0.028, p = 0.03); (B). Right cerebellum III (p = 0.026, p =
0.004); (C). Left paracentral lobule (p = 0.001, p = 0.01); (D). Left middle cingulate cortex (p =
0.01, p = 0.025); (E). Left pallidum (p < 0.001, p < 0.001).
45
Sex Differences in Activation
There were no sex differences in intensity of activation (PSC) in the activated
areas tested except right inferior temporal gyrus where men showed a greater PSC than
women during the 70% MVC [0.65 ± 0.25 vs. 0.32 ± 0.21 respectively; t(12)=2.623, p =
0.022].
Activation Volume (Voxel Number)
Several regions showed activation of additional voxels as force increased from
low to high intensities of contraction (See Table 2.2). These areas are detailed below.
Table 2.2. Brain Areas with Increased Activation Volume. Brain areas with increased
activation volume during submaximal contractions with the right ankle isometric dorsiflexor
muscles. Areas that had significant linear increases in voxel number with increased force are
shown.
Regions for CA_N27_ML atlas
L. Precentral Gyrus (primary motor cortex, M1)
R. Inferior Frontal Gyrus (p. Orbitalis)
L. Anterior Cingulate Cortex
L. Para Hippocampal Gyrus
L. Amygdala
R. Amygdala
L. Su. Occipital Gyrus
R. Su. Occipital Gyrus
R. Fusiform Gyrus
L. Postcentral Gyrus
R. Paracentral Lobule
L. Putamen
L. Thalamus
R. Inferior Temporal Gyrus
L. Cerebellum (IV-V)
R. Cerebellum (IV-V)
R. Cerebellum (VI)
Effect of Force
Linear Trend
p > 0.05
p = 0.02
p = 0.002
p > 0.05
p > 0.05
p > 0.05
p > 0.05
p = 0.037
p > 0.05
p > 0.05
p > 0.05
p = 0.059
p > 0.05
p = 0.053
p = 0.085
p = 0.04
p = 0.059
p = 0.029
p = 0.052
p = 0.033
p = 0.038
p = 0.047
p = 0.026
p = 0.069
p = 0.048
p = 0.044
p = 0.059
p = 0.083
p > 0.05
p = 0.094
p > 0.05
p > 0.05
p = 0.023
p > 0.05
R: Right; L: Light; Su: Superior; I: Inferior; The activation regions were thresholded with the
average PSC value of whole activation area.
46
Right inferior frontal gyrus (p. Orbitalis): Activation volumes were 3928 ± 1544, 3077
± 2010, 4663 ± 1973, 5091 ± 1930 voxels at 10, 30, 50, and 70% MVC intensities
respectively. Activation volume increased with a greater torque intensity [main effect of
torque intensity, F(3, 11) = 3.67, p = 0.020] in a strong linear trend [F(1, 13) = 4.01, p =
0.052].
Right superior occipital gyrus: Activation volumes were 2070 ± 1538, 1895 ± 1276,
3105 ± 1586, 2949 ± 1771 voxels at 10, 30, 50, and 70% MVC intensities respectively.
Activation volume increased with a greater torque intensity [F(3, 11) = 3.12, p = 0.037]
in a linear trend [F(1, 13) = 4.781, p = 0.048].
Right cerebellum (IV-V): Activation volumes were 1725 ± 910, 1543 ± 1308, 2157 ±
1045, 2580 ± 1032 voxels at 10, 30, 50, and 70% MVC intensities respectively.
Activation volume increased with a greater torque intensity [F(3, 11) = 3.12, p = 0.040]
in a linear trend [F(1, 13) = 6.64, p = 0.023].
Left anterior cingulate cortex: Activation volumes were 3363 ± 1432, 1975 ± 1260,
2471 ± 1186, 2120 ± 1099 voxels at 10, 30, 50, and 70% MVC intensities respectively. In
contrast to other areas, the left anterior cingulate cortex showed a decrease in activation
volume as torque intensity increased [main effect of torque intensity, F(3, 11) = 6.18, p =
0.002]. The decrease was linear [F(1, 13) = 5.69, p = 0.033] so that the activation volume
for 30, 50, and 70% MVC were significantly smaller than for the 10% MVC (p = 0.002,
p = 0.016, and p = 0.013, respectively).
47
Torque Fluctuations and BOLD PSC
To gain insight into which areas of the brain were associated with the torque
fluctuations during the submaximal contractions, we tested the association between the
brain activity (BOLD signal intensity) and the magnitude of torque fluctuations, first with
the SD of torque and then the CV. When all four intensities of contraction were pooled
into the one analyses (n = 56), there were significant associations between the absolute
torque fluctuations (SD) and mean PSC in several areas including the left putamen [r(56)
= 0.36, p =0.007], left calcarine gyrus [r(56) = 0.30, p = 0.027], right cerebellum [r(56) =
0.47, p < .001], M1/SMA (left paracentral lobule) [r(56) = 0.45, p < 0.001], left superior
frontal gyrus, [r(56) = 0.29, p = 0.03], right insula [r(56) = 0.28, p = 0.031], left
SMA/left middle cingulate cortex [r(56) = 0.43, p = 0.001], and left pallidum [r(56) =
0.42, p = 0.001] (Table 2.3). When the correlation analysis was performed at each
contraction intensity (n = 14), significant associations were found at the 10% MVC in the
left lingual gyrus [r(14) = -0.607, p = 0.021] , at the 30% MVC in the left occipital gyrus
[r(14) = 0.740, p = 0.002] and right insula lobe [r(14) = 0.650, p = 0.012].
Because the SD of force covaries with intensities of contraction, the torque
fluctuations were normalized to the mean torque (i.e. CV of torque) and associations with
the various brain areas determined. There were significant correlations with the mean
PSC in several areas including the right inferior parietal lobule [r(56) = -0.278, p =
0.038], right putamen [r(56) = -0.293, p = 0.028], left superior frontal gyrus [r(56) = 0.331, p = 0.013], and right insula [r(56) = -0.330, p = 0.013] (Table 2.4). When a
separate correlation analysis was performed for each contraction intensity, positive
correlations were found for the 10% MVC in the right inferior parietal lobule [r(14) = -
48
0.721, p = 0.004]; and for the 30% MVC, in the left superior frontal gyrus [r(14) = 0.548, p = 0.042], left postcentral gyrus [r(14) = -0.793, p = 0.001], left SMA [r(14) = 0.548, p = 0.042], and right middle supramarginal gyrus [r(14) = -0.672, p = 0.008].
Table 2.3. Correlations between torque fluctuations (SD) and PSC (N = 56; All
torque conditions pooled)
SD of
Force
SD of
Torque
L.
Putamen
L.
Calcarine
Gyrus
L.
M1 /
SMA
R.
Cereb
L. S.
Frontal
Gyrus
R.
Insula
L.M.
Cing
Cortex
1
L. Putamen
L.
Calcarine
Gyrus
R.
Cerebellum
.359**
1
.295*
0.251
1
.472**
.613**
.550**
1
L.
M1/SMA
.451**
.708**
.582**
.737**
1
L. Su.
Frontal
Gyrus
.290*
.617**
.530**
.674**
.815**
1
R. Insula
.277*
.428**
.735**
.492**
.700**
.696**
1
.433**
.545**
.680**
.668**
.693**
.546**
.654**
1
.418**
.426**
.421**
.601**
.553**
.479**
.384**
.561**
L.M.
Cingulate
Cortex
L.
Pallidum
L.
Pallidum
1
Note. Cereb: Cerebellum; Cing Cortex: Cingulate Cortex; L: Left; R: Right; Su: Superior, M: Middle; *
p < 0.05, **p < 0.01.
Table 2.4. Correlations between torque fluctuations (CV) and PSC (N
= 56; All torque conditions pooled)
CV of
Torque
CV of Torque
R. I. Parietal Lobule
R. I.
Parietal
Lobule
R.
Putamen
L. S.
Frontal
Gyrus
1
-.278*
1
-.293
*
.567**
L. Su. Frontal Gyrus
-.331
*
**
.615**
1
R. Insula
-.330*
.417**
.571**
.444**
R. Putamen
R. Insula
.477
1
Note. L: Left; R: Right; Su: Superior; I: Inferior; * p < 0.05, **p < 0.01.
1
49
DISCUSSION
This study determined those areas of the brain that were associated with lower
limb force and steadiness during isometric contractions over a range of intensities in
young men and women. We found that activation in the primary motor and sensory
cortices, basal ganglia and cerebellum scaled linearly with increased torque of the ankle
dorsiflexor muscles, and similarly in men and women. A unique and important finding of
this study was that several motor areas (basal ganglia, cerebellum, M1/SMA, and insula)
and some typically non-motor areas (superior frontal gyrus, cingulate cortex), increased
in activation as fluctuations in torque increased in higher intensity contractions.
Furthermore, in order to account for the increased intensity of contraction, we determined
those areas of the brain that were associated with the fluctuations in torque when
normalized to the mean target torque (CV). The results indicate that the putamen, insula,
contralateral superior frontal gyrus and ipsilateral inferior lobes play an important role in
control of steadiness of the lower limb during target matching contractions. The minimal
sex differences in brain activation during the steady isometric contractions explain the
similar steadiness of men and women during ankle dorsiflexion; these findings also
corroborate other work (Hunter et al., 2006) that indicates men and women are similarly
motivated and able to activate cortical centers during maximal and submaximal
performance of motor tasks.
Brain Areas Associated with Increased Contraction Intensity During Isometric
Ankle Dorsiflexion
50
This study extended the current literature that has examined cortical activation of
foot movements (Ciccarelli et al., 2005; Dobkin et al., 2004; Francis et al., 2009; Huda et
al., 2008; MacIntosh et al., 2004; Orr et al., 2008) by identifying those activated areas
that control for contraction intensity during an isometric task with the lower limb. The
BOLD PSC increased linearly as the level of contraction intensity increased for the lower
limb in several cortical and subcortical regions including contralateral primary motor
cortex/SMA, the putamen and palladium in the basal ganglia, the cingulate cortex, and
ipsilateral cerebellum, with the greatest differences occurring between the 10% and 70%
MVC contraction torques. Activation volume did not increase in several of the motor
regions (e.g. M1/SMA, basal ganglia) that showed greater PSC of the BOLD as
contraction force increased. Typically, the central nervous system uses two strategies to
increases force: recruitment of motor units and rate coding of the motor unit (De Luca et
al., 1982a; Monster & Chan, 1977; Van Cutsem et al., 1997). For the tibialis anterior
muscle recruitment of motor units is adopted to increase force across up to ~90% MVC
(Van Cutsem et al., 1997) which is a larger recruitment range than intrinsic muscles of
the hand such as the first dorsal interosseus which has a recruitment range to ~50% MVC
(De Luca et al., 1982a). Our findings suggest that increased force of the tibialis anterior
muscle and the recruitment of motor units are achieved by increased intensity of cortical
activation in several key motor areas. These areas are addressed below.
BOLD signal intensity in M1/SMA increased linearly with intensity of
contraction. This change in BOLD signal intensity for the low and moderate contraction
intensities was also observed in several fMRI studies of the hand (Noble et al., 2011;
Spraker et al., 2007; van Duinen et al., 2008). Although we did not see a significant
51
difference between 10 and 30% MVC as was observed by Keisker, et al, (2009) for
dynamic power grip task, the increase was linear across the forces indicating that brain
activation scaled appropriately between low and high forces to achieve greater motor unit
recruitment required to increase the contraction intensity.
The basal ganglia played a significant role in the control of force of the ankle
dorsiflexor muscles similar to what is shown for hand muscles (Kinoshita et al., 2000;
Spraker et al., 2007). BOLD signal activity during hand contractions can vary
substantially in the different nuclei of the basal ganglia according to the task
requirements such as task selection and prediction, and amplitude and rate of the force
(Prodoehl et al., 2009). For example, Spraker, et al (Spraker et al., 2007) found that both
the globus pallidus external (GPe) and the subthalamic nucleus scaled in activation
intensity with increasing force amplitude, however, this was not the case for the globus
pallidus internal (GPi), putamen and caudate nucleus. Our results show a linear increase
in activation intensity in the putamen with increased force with the lower limb muscles.
Although we did not divide the globus pallidus into internal and external portions as
others have for the hand exercise (Prodoehl et al., 2009; Spraker et al., 2007; Vaillancourt
et al., 2007), we showed that the globus pallidus (pallidum) had a linear increase in
BOLD signal with the increase of the force level as others have shown for the hand
(Grafton & Tunik, 2011; Spraker et al., 2007; Vaillancourt et al., 2004; Vaillancourt et
al., 2007).
The cerebellum scaled linearly in intensity of activation as contraction force
increased and in contrast to other motor areas, the volume of activation increased in this
motor region. Others have shown increased cerebellum activation with increased hand
52
muscle force (Keisker et al., 2009; Kuhtz-Buschbeck et al., 2008). The force-related
activation in our study was mainly found in the anterior portion of the cerebellum, which
is related to sensorimotor control (Manni & Petrosini, 2004; Stoodley & Schmahmann,
2009; Stoodley & Schmahmann, 2010). We also found force-related activation in the
ipsilateral anterior lobe (lobule III and IV) which is thought to be related to sensory
function in detecting pain (Iadarola et al., 1998). A growing number of studies support
the hypothesis that the cerebellum influences not only the sensorimotor control of
movement but also cognitive and emotional function (O'Reilly et al., 2010; Stoodley &
Schmahmann, 2010). Thus, the cerebellum along with other motor areas including the
primary motor cortex, SMA and basal ganglia play a key role in modulating intensity of
force of the lower limb, but the increase in activation and volume also may reflect its role
in sensorimotor integration.
Appropriate visual cortical centers were activated during the motor task
performance because participants were required to utilize visual feedback and trace a
horizontal cursor with the target torque presented via a rear-projection visual display
system. These visual areas activated however did not scale linearly with intensity of
contraction. The visual areas activated during right ankle dorsiflexion during all tasks
included the lingual gyrus, calcarine gyrus, and parietal lobule in both parietal and
occipital lobes and each of these areas is known to be active in visual processing. The
lingual gyrus was likely responsible for assisting with visual recognition of the target
(Gron et al., 2000), the calcarine gyrus (V1) for the central visual field and spatial
attention (Martínez et al., 1999) and the parietal lobule likely contributed to the ability of
53
participants to respond to visual sensory input with appropriate motor output (Clower et
al., 2001).
Cortical Areas Associated with Steadiness During an Isometric Contraction
A novel finding of this study was the identification of brain areas that control
maintenance of a steady contraction during a target matching task with the lower limb of
young adults. As expected the amplitude of the torque fluctuations (SD of torque)
increased linearly with the level of absolute contraction force (Galganski et al., 1993;
Moritz et al., 2005; Tracy, 2007a). Accordingly, those motor areas of the brain that
increased activation intensity between low and high forces, the basal ganglia, M1, SMA
and cerebellum, were also those associated with the SD of the torque in both men and
women. One exception was the positive correlation with the PSC in contralateral
calcarine gyrus where the primary visual cortex is located (Rajimehr & Tootell, 2008).
Despite the linear correlation found in the visual area when all intensity conditions were
pooled, the left lingual gyrus, which is also a visual area, showed a negative relation
when correlation analysis was performed separately at each contraction intensity. The
reduced activity of some visual areas as force increased may be related to the importance
of the visual feedback in maintaining a steady contraction at the lower forces, although in
young adults the effect of visual feedback is minimal compared with other populations
such as older adults (Tracy, 2007b).
Because the intensity of the PSC with increased SD of torque in several motor
areas covaried with the contraction intensity, we also examined the CV of torque
(amplitude of the fluctuations normalized to the mean torque). The CV of torque was
greatest at the lowest intensity of contraction (10% MVC) (see Fig. 2C) for both men and
54
women as found for several upper and lower limb muscles (Burnett et al., 2000;
Jesunathadas et al., 2012; Jones et al., 2002; Laidlaw et al., 1999; Taylor, A. M. et al.,
2003; Tracy, 2007b). Several areas including two motor areas, the putamen and
contralateral superior frontal gyrus, as well as the insula, and ipsilateral inferior lobes
were associated with the CV of force when all the contraction intensities were pooled.
The putamen, in the basal ganglia, also showed increased PSC as contraction intensity
increased (Fig 2.4A). Although not in this current study, the activation in the left superior
frontal gyrus was also large during a lower force task of the hand (Kuhtz-Buschbeck et
al., 2001), and was associated with force during a power grip task (Kuhtz-Buschbeck et
al., 2008). CV of force during isometric contractions is thought to be mostly mediated
by low-frequency oscillations in neural drive (< 2-3 Hz) (Dideriksen et al., 2012; Negro
et al., 2009) across a range of forces. Thus, our data raises possibility that these motor
regions, the putamen and the superior frontal gyrus, are sources or significant conduits of
the low-frequency oscillating neural drive that influences the trains of action potentials
and ultimately the CV of force.
Both the left superior frontal gyrus motor region and the ipsilateral inferior lobes
however showed the largest correlations between CV of force and activation for the 10%
MVC task, which is the intensity that CV was largest across the intensities (r = -0.55 and
-0.72 respectively). The ipsilateral parietal lobule is typically involved in oculomotor and
attention processes (Clower et al., 2001). This new finding with the lower extremity
control supports the previous observation that visual processes can have a large influence
on the motor variability and ability to hold a steady contraction at the lower forces
(Aagaard, 2003; Prodoehl & Vaillancourt, 2010; Sosnoff & Newell, 2006b; Tracy,
55
2007b). Further, at low forces up to 10%, synaptic noise and the resultant more variable
motor unit discharge rates can contribute to the large CV of force (Dideriksen et al.,
2012; Negro et al., 2009). Motor unit discharge rate variability, however probably has its
greatest influence on the CV at low forces in upper limb muscles and lesser effects in the
tibialis anterior (Jesunathadas et al., 2012). These findings also raise the possibility that
along with the superior frontal gyrus (motor region), the ipsilateral parietal lobule, is also
an important brain area involved in the increased CV at the lower forces during ankle
dorsiflexion.
No Sex differences in Brain Activation During Ankle Dorsiflexion
Although men were stronger than women, there were no differences in the
normalized motor performance tasks indicating that men and women contracted their
ankle dorsiflexor muscles at similar relative intensities of force and displayed similar CV
and changes in force fluctuations with contraction intensity. Although there are
widespread sex differences reported in the brain during cognitive and some motor tasks,
we found that activation was similar for young men and women during ankle dorsiflexion
for most motor areas. Only in the right inferior temporal gyrus did men display greater
activation than the women during the highest contraction intensity (70% MVC). The
greater activation of temporal gyrus for men in this study is consistent with that for a
finger tapping (Lissek et al., 2007). Our task was relatively simple; sex differences may
become more apparent when the task complexity and greater cognitive component are
involved (Lissek et al., 2007) or different muscle groups and postures are adopted. Our
results also indicate that men and women are similarly motivated and able to activate
cortical motor centers during static tasks over a range of forces.
56
Conclusion
Activation intensity (PSC) of several cortical and subcortical regions increased
with contraction intensity during static ankle dorsiflexion including the primary motor
cortex, basal ganglia and cerebellum. In general, activation volume did not increase in
motor areas that demonstrated greater activation intensity, and indicating a minimal role
of volume of activation in motor unit recruitment to achieve high forces. Activation of a
visual (ipsilateral parietal lobule) and motor (contralateral superior frontal gyrus) area
were associated with the greater torque fluctuations (CV) at low forces, and therefore
may play a role in control of steadiness especially during low intensity contractions in
young adults. Activation of the putamen (basal ganglia) was associated with both the CV
of force and contraction intensity suggesting this area also plays a central role in the
control of steady contractions across the range of forces. Although men were stronger
than women, they had similar normalized fluctuations in torque (steadiness) when
executed in the supine position and primarily similar areas and intensities of brain
activation across the range of low and high intensity contractions. The minimal sex
differences in brain activation during the steady isometric contractions indicate that
young men and women are equally motivated and able to activate cortical motor centers
during static tasks. Therefore, the key cortical and subcortical brain areas that were
identified in healthy men and women for control of lower limb steady contractions, could
be targeted in future to determine impaired such as occurs with aging and neurologic
conditions and enhanced function that can occur with physical training.
57
CHAPTER III
Women are less steady than men during low-force isometric
contractions of the lower extremity
SUMMARY
Although steady, controlled movements in the lower extremity are imperative
when performing a basic functional activity such as walking, it is unclear if changes in
cortical involvement during motor task performance influences lower limb force
production and control in men and women differently. To expose sex differences in
cortical involvement during motor performance, we compared steadiness (force
fluctuations) and fatigability of submaximal isometric contractions with the ankle
dorsiflexor muscles in young men and women and with varying levels of cognitive
demand imposed. Sixteen young (8 men, 21.5 ± 2 yr., 8 women, 19.3 ± 1.5 yr.) attended
three sessions in which they performed a 40 s isometric contraction at 5% maximal
voluntary contraction (MVC) force followed by an isometric contraction at 30% MVC
until task failure. The cognitive demand required during the submaximal contractions in
each session differed as follows: 1) high-cognitive demand session where difficult mental
math was imposed (counting backward by 13 from a 4-digit number); 2) low-cognitive
demand session which involved simple mental math (counting backward by one); and 3)
control session with no mental math. Anxiety was elevated during the high-cognitive
demand session compared with other sessions more so for the women than men (p<0.05).
Women demonstrated greater force fluctuations than men during the very low (5% MVC)
58
force task (p = 0.005) and the moderate-force (30% MVC) fatiguing contraction (p =
0.002) regardless of cognitive demand compared with men. Women also demonstrated a
similar time to task failure as men for the moderate-force task regardless of cognitive
demand (6.2 ± 2.4 and 6.7 ± 2.1, respectively; p = 0.060). These findings suggest that
both young men and women are more able to successfully achieve a difficult cognitive
task and a target matching motor task with the lower limb than with the upper limb.
INTRODUCTION
Decreased steadiness (increased force fluctuations) during sustained low-tomoderate force isometric contractions can negatively impact achievement of a goaldirected movement (Enoka et al., 2003), and potentially interfere with performance of
work-related or functional tasks. For example, decreased hand steadiness diminishes
manual task precision (Endo & Kawahara, 2011) and decreased ankle steadiness may
increase postural sway in the lower extremity (Kouzaki & Shinohara, 2010), potentially
leading to increased fall risk particularly in adults with neurological disorders such as
multiple sclerosis (Cameron & Lord, 2010; Findling et al., 2011) and with older adults
(Fernie et al., 1982). The ability to sustain steady low and moderate force contractions
can be influenced by a variety of factors which can include advanced age (Galganski et
al., 1993; Laidlaw et al., 2000), fatigue (De Luca et al., 1982b; Singh et al., 2010),
arousal (Lorist et al., 2002; Noteboom, Barnholt, et al., 2001), intensity and type of
contraction (Cresswell & Loscher, 2000; Griffith et al., 2010; Semmler et al., 2007), and
muscle group (Hunter, Yoon, et al., 2008; Jesunathadas et al., 2012; Tracy, Mehoudar, et
al., 2007). The participant’s sex may also contribute to decreased steadiness when
59
contractions are sustained at the same relative intensity; however the influence of sex is
still unknown.
It is unclear if there is a steadiness sex difference because much of the literature
on steadiness neglects to examine if sex differences exist or reports mixed results. For
example, when performing submaximal isometric contractions, women are reported to be
more (Endo & Kawahara, 2011), less (Brown et al., 2010; Brown et al., 2009; Grunte et
al., 2009), or similarly (Baweja et al., 2009) steady compared with men. For example,
women demonstrated greater overall force fluctuations during isometric elbow flexion
contractions than men across multiple force levels (between 2.5 and 75% of maximal
voluntary contraction (MVC) force (Brown et al., 2010). In contrast, Tracy (2007a)
found differences in force fluctuations between opposing ankle muscle groups (ankle
dorsiflexion and plantarflexion) but no sex differences between young or older adults for
forces ranging between 2.5% and 80% MVC force. The first study of this dissertation
showed minimal differences in CV of force between the sexes for the ankle dorsiflexors
in a stable supine position. Given the disparity of results and the lack of attention to the
possibility of sex differences in steadiness, it remains unclear if women are less steady
than men when performing submaximal isometric contractions.
Mechanism responsible for force fluctuations across the range of forces between
low and high intensities isometric contractions is primarily the low frequency oscillations
of trains of motor units with some contribution of the motor neuron discharge rate
variability and motor unit properties especially at low forces (Barry et al., 2007;
Dideriksen et al., 2012; Jesunathadas et al., 2012; Moritz et al., 2005; Negro et al., 2009).
At low forces (< 10% MVC) several muscles including those in the lower limb have
60
larger force fluctuations normalized to the target force (CV of force) than moderate to
high force fluctuations (Dideriksen et al., 2012). The low frequency oscillations of the
trains of motor units appear to originate from descending and afferent inputs (Negro et
al., 2009). The first study of this dissertation suggests some of those descending inputs to
the motor neuron pool may originate from or are associated with both motor and nonmotor areas of the brain including the ipsilateral parietal lobule, putamen, insula, and
contralateral superior frontal gyrus (Chapter 2) (Yoon et al., 2014). How these inputs
differ between populations (e.g. men and women, young and older adults) who differ in
motor unit properties (Doherty, 2003; Hunter, 2014), and their effects on steadiness is not
known.
Altering descending inputs to the motoneurone pool occurs during altered states
of arousal and fatigue. Women have reduced steadiness with upper limb muscles
compared with men when arousal is increased by electric shocks to the back of the hand
(Christou et al., 2004), and increased cognitive demand (mental math) (Noteboom,
Fleshner, et al., 2001). Further, for the elbow flexor muscles, steadiness is reduced and
fatigability increased for both men and women when the cognitive demand is imposed
during a low-force (20% MVC) isometric task (Keller-Ross, Pruse, et al., 2014; Yoon et
al., 2009). However, women demonstrated increased in fatigability compared with men
and the increased fatigability was related to initial absolute strength (Keller-Ross, Pruse,
et al., 2014; Yoon et al., 2009). Whether such differences in fatigability exist for a lower
limb muscle where the number of corticospinal connections are less than for the upper
limb (Brouwer & Ashby, 1990) and fatigability is less dependent on initial strength than
the elbow flexor muscles (Hunter, 2014) is not known.
61
The first purpose of this study was to determine the influence of increased
cognitive demand on the time to task failure for a submaximal contraction of the ankle
dorsiflexors in men and women. The second purpose was to examine the influence of
varying levels of cognitive demand on the amplitude of force fluctuations during a very
low force (5% MVC) and moderate-force (30% MVC isometric contractions in the ankle
dorsiflexor muscles in men and women. Increased cognitive demand has been shown to
increase force fluctuations in women more so than in men prior to contractions in the
elbow flexors; thus, we hypothesize that women will demonstrate greater force
fluctuations than men during ankle dorsiflexion and this would increase even further with
exposure to high cognitive demand.
MATERIALS AND METHODS
Sixteen young adults (8 men, 8 women; 18 - 24 years) participated in the study
(see Table 3.1 for physical characteristics). All participants were healthy with no known
neurological or cardiovascular diseases and were naïve to the protocol. Both men and
women had low-to-moderate levels of anxiety (trait) (29.0 ± 8.1; 34.8 ± 7.9; p 0.16)
according to the State-Trait Anxiety Inventory (STAI) (Spielberger, 2010) and reported
no history of or current mental or psychological pathology, including anxiety or
depressive disorders. Participants were right-leg dominant (0.72 ± 0.22 vs.0.77 ± 0.17 for
men and women respectively, with a ratio of 1 indicating complete right-leggedness) as
estimated using the Edinburgh Handedness Inventory (Oldfield, 1971). The physical
activity level for each participant was assessed with a questionnaire that estimated the
relative kilocalorie expenditure of energy per week (Kriska & Bennett, 1992). Prior to
62
participation, each subject provided informed consent, and the protocol was approved by
the Institutional Review Board at Marquette University.
63
Table 3.1. Participant Physical Characteristics and Results. Participant characteristics and age group
means for control, low-cognitive demand (Low-CD) and high-cognitive demand (High-CD) sessions for
women and men. Variables reaching significance for main effect of sex are indicated by the asterisk (*).
Variable
Number of subjects
Age (years)
Height (cm)
Weight (kg)
Physical Activity (PAQ)
Baseline Trait STAI Scores
Baseline State STAI Scores
Session
Control
Low-CD
MVC Torque* (Nm)
High-CD
Total
Control
MVC Torque Recovery
Low-CD
(mean % of initial)
High-CD
Total
Control
Low-CD
Time to Task Failure (min)
High-CD
Total
Control
Low-CD
30% MVC CV of Torque* (%)
High-CD
Total
Control
Low-CD
TA EMG* (% MVC)
High-CD
Total
Control
TA EMG Bursting Activity
Low-CD
(bursts/min)
High-CD
Total
Control
Coactivation Ratio – TA:Gastroc Low-CD
High-CD
Control
Coactivation Ratio – TA:Soleus
Low-CD
High-CD
Women
Men
8
8
19.3 ± 1.5
21.5 ± 2
170 ± 6.2 167.8 ± 33.9
60.1 ± 6.0 88.8 ± 34.1
73.3 ± 44.7 45.7 ± 26.7
39.1 ± 7.5 33.8 ± 5.7
34.9± 7.9
29 ± 8.1
16.8 ± 2.7
28.1 ± 4.9
17.01 ± 2.4 27.3 ± 5.3
16.9 ± 2.4 28.6 ± 6.5
16.9 ± 2.5 28.0 ± 5.4
95.6 ± 3.1% 96.8 ± 10.3%
94.1 ± 2.3% 96.6 ± 4.5%
94.5 ± 3.9% 95.1 ± 4.1%
96.2 ± 6.6% 94.7 ± 3.1%
6.4 ± 1.7
5.8 ± 2.6
6.1 ± 2.2
6.1 ± 1.9
7.4 ± 2.5
6.8 ± 2.7
6.7 ± 2.1
6.2 ± 2.4
5.4 ± 1.1
3.6 ± 0.5
5.1 ± 0.8
3.8 ± 0.4
5.3 ± 0.7
3.9 ± 0.6
5.26 ± 0.8
3.8 ± 0.5
23.2 ± 3.3 23.7 ± 5.6
23.2 ± 3.1 23.6 ± 6.8
25 ± 2.4
23.3 ± 3.9
23.8 ± 2.9 23.6 ± 5.3
8.1 ± 8.2 12.5 ± 11.1
12.2 ± 10.8 10.7 ± 11
6.8 ± 7.8 12.0 ± 10.9
9.0 ± 8.9 11.7 ± 10.5
1.5 ± 1.1 0.93 ± 0.25
1.2 ± 0.51 0.92 ± 0.39
1.0 ± 0.36 0.87 ± 0.15
0.87 ± 0.49 0.91 ± 0.29
0.77 ± 0.25 0.80 ± 0.21
0.76 ± 0.30 0.71 ± 0.16
P
0.024*
0.861
0.034*
0.156
0.129
0.163
0.0001*
0.0001*
0.0001*
0.0001*
0.757
0.19
0.787
0.476
0.575
0.974
0.627
0.748
0.001*
0.002*
0.001*
0.0001*
0.826
0.885
0.308
0.882
0.381
0.791
0.292
0.473
0.142
0.231
0.254
0.84
0.809
0.672
* Variables reaching statistical significance for main effect of age (p < 0.05). PAQ = Physical Activity
Questionnaire, STAI = State-Trait Anxiety Index, MVC = Maximal Voluntary Contraction, CV =
Coefficient of Variation, TA = Tibialis Anterior, Gastroc = Gastrocnemius, EMG = Electromyography.
64
Each participant reported to the laboratory on four occasions to perform a
protocol that involved a fatiguing contraction with the left ankle dorsiflexor muscles:
once for a familiarization session and three experimental sessions (control, low-cognitive
demand and high-cognitive demand sessions), with each experimental session being at
least 5 days apart. During the low-cognitive demand and high-cognitive demand
sessions, each participant performed either a simple mental math task (low-cognitive
demand session) or difficult mental math task (high-cognitive demand session) at rest,
and also while performing isometric contractions at 5% maximum voluntary contraction
(MVC) force (40 s duration) and a 30% MVC for as long as possible until task failure
(Figure 3.1). During the control session, each participant performed the motor tasks
without performing any mental math. Session order was counterbalanced among
participants within each age group.
Figure 3.1. Experimental protocol. The top panel illustrates the order of tasks performed by each
participant with the ankle dorsiflexor muscles. Maximal voluntary contractions (MVC) (solid bars) were
performed at the beginning of the experimental session and during recovery (immediately after the
fatiguing contraction and at 1, 2, 5, and 10 min of recovery). The fatiguing contraction (30% MVC),
symbolized by the hatched rectangle, was performed until task failure by each participant. The bottom
panels indicate with arrows when the State-Trait Anxiety Inventory (STAI; 4 times) was performed and the
Visual Analog Scale (VAS) for anxiety and stress were recorded (at 7 time points, T1 – T7). The
schematic is not to scale for time or force.
65
Mechanical Recording of Force. Each participant was seated upright in an
adjustable chair (Biodex Medical Systems, NY) with the hip and knees at 90° of flexion.
The setup is similar to that described elsewhere (Griffith et al., 2010). In brief, the left
foot rested on a footplate in a custom made dynamometer to measure forces of the lower
leg, with the ankle in a neutral position (0° dorsiflexion). The foot was secured to the
footplate via a strap placed over the anterior aspect of the ankle and another strap placed
1–2 cm proximal to the metatarsophalangeal joint. Isometric force of the dorsiflexor
muscles was recorded using a force transducer (Transducer Techniques, Temecula, CA)
and recorded online at 500 Hz using a Power 1401 analog-to-digital (A/D) converter and
Spike2 software (Cambridge Electronics Design (CED), Cambridge, UK). Force
displayed on a 19-inch monitor was located at eye level 1.5 m in front of the participant.
Each participant was asked to trace a horizontal cursor placed in the middle of the screen
with the force signal as it appeared on the screen from the right side of the monitor.
Electrical Recordings. Whole muscle EMG signals of the tibialis anterior, medial
head of the gastrocnemius, soleus, and rectus femoris were recorded using bipolar surface
electrodes (sintered pellet Ag-AgCl, 8mm diameter, with 20 mm between electrodes)
taped to the skin over the bellies of each muscle. Reference electrodes were placed on
the patella. The recording electrodes on each muscle were placed in line with the muscle
fibers and in accordance with locations recommended by the European
Recommendations for Surface Electromyography (Hermens et al., 2000). The EMG
signals were amplified (1,000 x) and band-pass filtered (13 –1,000 Hz) with Coulbourn
bioamplifiers (Coulbourn Instruments, Allentown, Pennsylvania) prior to being recorded
directly to a computer using the Power 1401 and Spike2 software (CED). The EMG
66
signals were digitized at 2,000 samples/s and analyzed offline using Spike2 software
(CED).
Cardiovascular Measurements. Heart rate and blood pressure were monitored
during submaximal and fatiguing contractions and periods of rest or mental math with an
automated beat-by-beat blood pressure monitor (Finapres 2300, Datex-Ohmeda,
Louisville, CO). The blood pressure cuff was placed around the middle finger of the left
hand, and the arm was placed on a platform to maintain the hand at heart level. Blood
pressure was sampled at 500 samples/s and collected online to PC using Spike2 software
(CED).
Cognitive Assessment of Anxiety and Stress. Cognitive levels of anxiety and stress
were assessed throughout the protocol using a visual analogue scale (VAS) (Yoon et al.,
2009) and the state portion of the STAI questionnaire (Spielberger, 2010). Each VAS
(one for anxiety and another for stress) had a 10-cm line anchored at the far left by
“none” and at the far right by “as bad as it could be.” The right anchor corresponded to
the most stressful or most anxious moment in the life of the participant. Anxiety was
defined as the participant’s negative feelings regarding the immediate future, whereas
stress represented the physical changes (e.g., increase in heart rate and perspiration)
occurring during the test perceived by the participant that were above and beyond the
expectation for their level of exertion (Christou et al., 2004). VAS for anxiety and stress
were recorded at seven time points (T1 - T7) during the protocol: one baseline assessment
before intended arousal (T1); during the rest period after each 2 × 2-min bout of mental
math (low-cognitive demand or high-cognitive demand session) or quiet rest (control
session) (T2, T3); immediately after the 5% MVC submaximal contraction (T4);
67
immediately after the fatiguing contraction/MVC (T5); and 5 and 10 min after the
fatiguing contraction (T6, T7) (Figure 3.1).
The state portion of the STAI- questionnaire consisted of 20 statements that
required a response on a four-point Likert-type scale. Baseline trait and state assessments
were conducted during the familiarization session. There was no significant difference
between young and older adults in baseline trait STAI scores (p = 0.54) or baseline state
STAI scores taken during the familiarization session (p = 0.66) (Table 3.1). State STAI
assessments were also conducted at four different time points during the experimental
protocol: baseline assessment before arousal; after 2 × 2-min bouts of quiet sitting
(control session) or mental math (low-cognitive demand and high-cognitive demand
sessions); immediately after the fatiguing contraction/MVC; and 10 min after completion
of the fatiguing contraction (Figure 3.1).
Cognitive Demand Conditions. Difficult mental math is an established
psychosocial technique used to induce cognitive demand (Kajantie & Phillips, 2006) and
was used for the high-cognitive demand task (Noteboom, Fleshner, et al., 2001). Each
participant performed serial subtraction from a four-digit number by 13 with one
response required every 3 s (Noteboom, Fleshner, et al., 2001). If the participant made an
error in serial subtraction or was unable to provide the correct answer within 3 s, they
were asked to restart the mental math from the first number in the series. After three
errors, the investigator asked the participant to begin with a new four-digit number. The
simple mental math task, performed during the low-cognitive demand session, was
designed to increase cognitive demand above control without elevating arousal.
Participants serially counted backward by 1’s from 50 to 0 at a slow, even pace. If the
68
participant made an error in counting, they were asked to restart counting from 50.
During the control session participants were instructed to rest quietly during the 2 × 2min bouts, 5% MVC submaximal contraction (40 s) and 30% MVC fatiguing contraction.
During the low-cognitive demand and high cognitive demand sessions, participants
performed the mental math task while at rest (2 × 2-min bouts), and then continuously
during the 5% MVC submaximal contraction and 30% MVC fatiguing contraction until
task failure.
Experimental protocol. The protocol for each experimental session (control, lowcognitive demand and high-cognitive demand sessions) involved procedures in the
following order: 1) MVCs of the ankle dorsiflexor, ankle plantarflexor and knee extensor
muscles; 2) assessment of cognitive and physiological arousal before and after 2 × 2-min
bout of either quiet sitting (control session), simple mental math (low-cognitive demand
session), or difficult mental math (high-cognitive demand session); performance of: 3)
one submaximal isometric contraction at 5% MVC force sustained for 40 s with
assessment of cognitive and physiological arousal immediately following the contraction;
4) a submaximal fatiguing isometric contraction at 30% MVC force sustained until task
failure; and 5) recovery MVCs immediately following the fatiguing contraction, and at 1,
2, 5, and 10 min recovery with assessment of anxiety and stress levels (Figure 3.1).
Participants performed two MVCs of the knee extensor and plantar flexor muscles
each at the beginning of each experimental session in order to obtain peak EMG for the
gastrocnemius, soleus and rectus femoris muscles. Participants rested for 60 s between
each trial. For both muscle groups, MVCs were performed with the participant seated in a
position the same as for testing the ankle dorsiflexors muscles (described above). The aim
69
was to obtain peak EMG values for each muscle group: forces were not recorded during
these contractions for knee extension and plantar flexion. Each participant was asked to
push as hard as possible against an immovable restraint for 3–4 s to activate either the
knee extensor or ankle plantar flexor muscles. For the knee extensor muscles, manual
resistance was applied to the distal leg (just above the lateral malleolus) so that the lower
leg was restrained at 90° of flexion while the participant performed maximal knee
extension. For the ankle plantar flexor muscles, the foot of each participant was placed
on the footplate, and vertical movement was minimized during each MVC by a block that
eliminated movement of the footplate. The MVC trial with the greatest amount of EMG
activity was used to normalize the EMG recordings during the fatiguing contractions of
the rectus femoris, medial head of the gastrocnemius, and soleus muscles.
Participants performed three to four MVC trials with the ankle dorsiflexors while
their foot was attached to the footplate. Each participant was asked to dorsiflex as hard as
possible for 3–4 s. Participants were given visual feedback on a display monitor and
strong verbal encouragement to achieve and maintain maximal force. Participants rested
for 60 s between each trial. If the peak force achieved for two of the first three trials was
not within 5% of each other, additional trials were performed until this criterion was met.
The greatest MVC force achieved with the ankle dorsiflexor muscles was used as the
reference to calculate the target level for both the submaximal contractions at 5% MVC
and the fatiguing contraction at 30% MVC. The MVC with the greatest amount of EMG
activity was used to normalize the EMG recordings during the fatiguing contractions of
the tibialis anterior muscle. MVCs of the ankle dorsiflexor muscles were also performed
during recovery (Figure 3.1).
70
A fatiguing contraction was performed with the ankle dorsiflexor muscles at 30%
MVC during each experimental session. Each participant was asked to trace a horizontal
cursor with the force signal as it appeared on the screen from the left side of the monitor
in order to match the vertical target force as displayed on the monitor. Participants were
encouraged to sustain the force for as long as possible. The fatiguing contraction was
terminated when the force declined by 10% of the target force. To minimize the influence
of transient fluctuations in motor output on the criteria for task failure, the task was
terminated only after force fell below the predetermined threshold for 2.5 s of a 5 s
interval. Participants were not informed of their time to task failure.
Rating of perceived effort (RPE) was assessed using the modified Borg 10-point
scale (Borg, 1982). Each participant was instructed to focus their assessment of effort on
the ankle muscles performing the fatiguing task. The scale was anchored so that 0
represented the resting state and 10 corresponded to the strongest contraction that the
ankle muscles could perform. The RPE was recorded at the beginning of the fatiguing
contraction and every minute thereafter until task failure. In order to obtain RPE while
the participant was performing mental math during the low-cognitive demand and highcognitive demand sessions, the participant was interrupted and asked to report their RPE.
After reporting their RPE, participants resumed the mental math task of serial counting
from 50 to 0 during the low cognitive demand session or serial subtraction using a new 4digit number during the high cognitive demand session.
Data Analysis
All data collected during the experiments were recorded online using a Power
1401 A/D converter and analyzed using Spike2 (CED). The MVC torque was quantified
71
as the average value over a 0.5-s interval that was centered about the peak. The torque
for the MVCs, submaximal and fatiguing contractions was calculated as the product of
force and the distance between the ankle joint and the point at which the ankle was
attached to the force transducer. The maximal EMG for each muscle was determined as
the root mean square (RMS) value over a 0.5-s interval about the same peak interval of
the MVC torque measurement. The maximal EMG value of the involved muscles was
then used to normalize the RMS EMG values recorded during the fatiguing contraction.
The RMS value of the 5% MVC dorsiflexion task was averaged for each muscle over the
middle 30 s of the 40 s contraction for the tibialis anterior, medial gastrocnemius, soleus
and rectus femoris. During the 30% MVC fatiguing contraction, the RMS EMG signal
for each muscle was quantified at the following time intervals: the first 30 s; 15 s on both
sides of 25, 50, and 75% of time to task failure; and the last 30 s of the task duration. The
EMG activity of each muscle was normalized to the RMS EMG value obtained during
the MVC for each respective muscle. The level of coactivation was quantified by
calculating the ratio between the RMS EMG (% peak) of the agonist muscle (tibialis
anterior) and antagonist muscle (medial gastrocnemius or soleus) (Coactivation =
antagonist/agonist x 100) [e.g. (Griffith et al., 2010)].
To quantify the bursts of EMG activity of the tibialis anterior during the 30%
MVC fatiguing contraction, the EMG signal was first rectified, smoothed (averages of 1-s
duration, 500 data points), and then differentiated over 0.25-s averages. The
differentiated signal represents the rate of change and was used to identify rapid changes
in the rectified and smoothed EMG signal. The threshold for establishing if a burst of
EMG had occurred was determined by first finding the minimum SD of the differentiated
72
EMG during the fatiguing contraction using a 30-s moving window; the threshold was
then defined as the mean + 3 SD of the minimum differentiated signal. The minimal burst
duration was 0.1 s. The EMG bursting activity (bursts/min) was quantified for five
continuous intervals of 20% of the time to task failure.
The amplitude of the force fluctuations was quantified as the coefficient of
variation (SD/mean × 100) for the 5% MVC task and 30% MVC fatiguing contraction.
The fluctuations in force during the 5% MVC task were quantified over the middle 30 s
of the 40 s contraction and for the fatiguing contraction at 30% MVC for five continuous
intervals of 20% of the time to task failure.
Mean arterial pressure (MAP) and heart rate (HR) were evaluated only on
participants not currently taking blood pressure medications with normal blood pressure.
MAP and HR recorded during the 30% MVC fatiguing contraction were analyzed by
comparing ~15 s averages at 25% intervals throughout the fatiguing contraction; during
the 5% MVC submaximal contraction they were quantified over the middle 30s of the
40s contraction. For each interval, the blood pressure signal was analyzed for the mean
peaks [systolic blood pressure (SBP)], mean troughs [diastolic blood pressure (DBP)],
and number of pulses per second (multiplied by 60 to determine heart rate). MAP was
calculated for each epoch with the following equation: MAP = DBP + ⅓ (SBP - DBP).
Rate pressure product (RPP) was calculated as the product of heart rate and MAP for the
equivalent time periods as stated above.
Statistical Analysis
Data were reported as means ± SD within the text and displayed as means ± SE in
the figures. Repeated measures ANOVAs, with sex (men and women) as a between-
73
subject factor, were used to compare the various dependent variables. Repeated measures
factors during different tests included session (control, low-cognitive demand and highcognitive demand) and either fatigue (pre-, post-fatiguing contraction), recovery
(immediately after fatiguing task failure, 1 min, 2 min, 5 min, and 10 min after fatiguing
task failure) or time (state STAI scores were taken at baseline, after 2 × 2-min of quiet
sitting or dual task, fatiguing contraction and 10 min recovery; VAS was taken at
baseline, after each 2 min of quiet sitting or dual task, 5% submaximal contraction,
immediately after task failure, and at 5 min and 10 min of recovery (T1 – T7; Figure 3.3).
Post hoc analyses (Tukey) were used to test for differences among pairs when
appropriate. Independent t-tests (one-tailed) were used to compare subject physical
characteristics, percent decline in MVC force across the fatiguing tasks, and rates of
increase in various dependent variables as a function of absolute time. A significance
level of p < 0.05 was used to identify statistical significance. Time to task failure was
compared across sessions using repeated measures ANOVA with sex as a between group
factor. The contribution of several variables to time to task failure was analyzed using
multiple linear regressions. These variables included the rate of change in RMS EMG
activity of each muscle, EMG bursting activity of the tibialis anterior, MAP, heart rate,
RPE, fluctuations in force, and MVC force (SPSS version 22).
RESULTS
MVC Torque
At baseline, men were stronger than women (28.0 ± 5.4 Nm vs. 16.9 ± 2.5 Nm;
sex effect, p = 0.0001) on all three days of testing (Table 3.1). The relative reduction (%)
74
in MVC torque after the fatiguing contraction was similar across sessions (session effect,
p = 0.539), and similar for men and women (sex effect, p = 0.0476). During recovery,
MVC torque increased to near baseline levels within 10 minutes of completing the
fatiguing contraction similarly for men and women across all sessions (session × sex, p =
0.407; Table 3.1). Furthermore, at the end of the recovery period, relative strength was
similar men and women (p > 0.05).
Arousal
State STAI Scores. Men and women demonstrated similar baseline state STAI
scores prior to each experimental session (p = 0.163; Table 3.1). State STAI scores taken
immediately after the 2×2-min bout were significantly elevated after the high-cognitive
demand session compared with the control and low-cognitive demand sessions (p <
0.0001), but there was no interaction (session × sex, p = 0.064), or difference between
men and women (sex effect, p = 0.140). State STAI scores taken immediately after the
fatiguing were significantly elevated for women compared to men across sessions
(session × sex, p = 0.003; Figure 3.2), but there was no main effect of sex (p = 0.318);
scores taken during recovery were similar across sessions for men and women (session, p
= 0.349; sex effect, p = 0.995; session × sex, p = 0.334). State STAI scores were higher
for women than men after the fatiguing contraction during the high-cognitive demand
session (women, 56.6 ± 11.9; men, 42.6 ± 11.1; p = 0.029).
75
State STAI Scores
Men
Women
44
State STAI Score
42
40
38
36
34
32
30
28
26
24
Control
Low-CD
High-CD
Session
Figure 3.2. State STAI scores. State STAI scores for men (closed symbols) and women (open
symbols) during the control, low-cognitive demand (low-CD) and high-cognitive demand (highCD) sessions. The values shown are mean ± SE. State STAI scores taken immediately after the
fatiguing were significantly elevated for women compared to men across sessions (session × sex,
p = 0.003).
VAS for Stress and Anxiety. There was no difference between men and women for
baseline stress and anxiety VAS taken at the beginning of each session (p > 0.05). Stress
and Anxiety VAS were greater during the high-cognitive demand session than control
and low-cognitive demand sessions (session, p = 0.0001), and over time (time, p = 0.001,
p = 0.002), but there were no sex differences (sex effect, p = 0.744, p = 0.899) and no
interactions (p > 0.05). VAS for stress and anxiety were elevated after the 2×2 bout, and
5% MVC contraction during the high-cognitive demand session compared with the
control and low-cognitive demand sessions (session, p < 0.05), but there were no sex
differences (sex effect, p > 0.05) and no interactions (session × sex, p > 0.05; Figure 3.3).
Both stress and anxiety scores were significantly elevated after the fatiguing contraction
76
across all three sessions (session, p = 0.0001), and although there were no interactions for
stress (session × sex, p > 0.05), anxiety was significantly higher for women across
sessions than men (session × sex, p = 0.040).
A
Men Control
Women Control
Men Low-CD
Women Low-CD
Men High-CD
Women High-CD
60
Anxiety VAS (cm)
50
40
30
20
10
0
-10
T1
T2
T3
B
T4
T5
T6
T7
T5
T6
T7
Time
60
Stress VAS (cm)
50
40
30
20
10
0
-10
T1
T2
T3
T4
Time
Figure 3.3. Visual Analogue Scale (VAS) scores for anxiety (A) and stress (B). Mean (±SE) VAS
scores for young adults (closed symbols) and older adults (open symbols) are shown for anxiety (A) and
stress (B) throughout the experimental protocol during the control session (circles), low-cognitive demand
session (Low-CD, triangles), and high-cognitive demand session (High-CD, squares). Time intervals were
as follows: baseline (T1), after the first bout of 2 min of quiet rest/mental math (T2), after the second bout
of 2 min of quiet rest/mental math (T3), after the 5% submaximal contraction (T4), during recovery
immediately after task failure (T5), and then at 5 min (T6) and 10 min of recovery (T7).
77
Low-Intensity Sustained Contraction (5% MVC)
Fluctuations in Torque. Women had greater fluctuations in torque (CV) than men
across all three sessions (sex effect, p = 0.005). Women demonstrated greater
fluctuations in torque than men because the interaction did not reach significance (linear
trend interaction, session × sex, p = 0.060; Figure 3.4). Fluctuations in force were
negatively correlated with strength during the control session (r2 = -0.863, n = 15, p =
0.0001), and the low-cognitive demand session (r2 = -0.719, n = 15, p = 0.002), but not
during the high-cognitive demand session (r2 = -0.437, n = 15, p = 0.104).
15
Men
Women
14
CV (%)
13
12
11
10
9
8
7
Control
Low-CD
High-CD
Session
Figure 3.4. Mean Session Coefficient of Variation (CV) of Torque during the 5% MVC Task. Mean
Session Coefficient of Variation (CV) of Torque during the 5% MVC Task for men (closed symbols) and
women (open symbols) during the control, low-cognitive demand (Low-CD), and high-cognitive demand
(High-CD) sessions. Women had greater fluctuations in torque (CV) than men across all three sessions (sex
effect, p = 0.005) during the 5% MVC task.
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Mean arterial pressure and heart rate. Mean arterial pressure during the 5%
MVC contraction was significantly higher during the high-cognitive demand session
(99.4 ± 14.0 mmHg) than the low-cognitive demand session (93.9 ± 8.9 mmHg) and the
control session (86.4 ± 10.1 mmHg; session effect, p = 0.003), but there was no influence
of sex on mean arterial pressure (sex main effect, p = 0.258). Heart rate was similar
across sessions (p = 0.073) for men and women (p = 0.73). Rate pressure product was
significantly higher during the high-cognitive demand session (session, p = 0.002), but
there was no difference between men and women (p = 0.925).
EMG Activity. Gastrocnemius, rectus femoris, soleus, and tibialis anterior RMS
EMG (% MVC) were similar across sessions (p = 0.077, p = 0.446, p = 0.159, p = 0.670,
respectively) for men and women (p = 0.855, p = 0.373, p = 0.534, p = 0.492,
respectively).
Fatiguing Contraction (30%MVC)
Time to Task Failure. There was no difference in time to task failure across
sessions (p = 0.060), interactions (sex × sessions, p = 0.748), or between men and
women (sex effect, p = 0.675; Table 3.1 and Figure 3.5). There was no correlation
between strength and time to task failure for any of the sessions.
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Time to Failure (min)
10
Men
Women
8
6
4
Control
Low-CD
High-CD
Session
Figure 3.5. Time to task failure during the 30% MVC Task. Time to task failure during the 30% MVC
task for men (closed symbols) and women (open symbols) are shown during the control, low-cognitive
demand (Low-CD), and high-cognitive demand (High-CD) sessions. There was no difference in time to
task failure across sessions (p = 0.060) or between men and women (sex effect, p = 0.675).
Fluctuations in Torque. Fluctuations in torque (CV) increased over time across
sessions (session × time, p = 0.0001) and women had significantly higher fluctuations in
torque than men (sex effect, p = 0.002; Figure 3.6). CV of torque was significantly
higher during the high-cognitive demand session over time (session × time, p = 0.030)
particularly for women (sex effect, p = 0.0001; Figure 3.6). Women demonstrated
greater fluctuations in torque than men throughout the fatiguing contraction. CV of
torque was negatively correlated with strength during the low-cognitive demand session
(r2 = -0.642, n = 16, p = 0.007) and the high-cognitive demand (r2 = -0.556, n = 16, p =
0.025) sessions, but not during the control session (r2 = -0.431, n = 16, p = 0.095).
80
Men Control
Women Control
Men Low-CD
Women Low-CD
Men High-CD
Women High-CD
8
CV of Torque (%)
7
6
5
4
3
2
1
0
20
40
60
80
100
Time (%)
Figure 3.6. Coefficient of variation (CV) of force during the fatiguing contraction. Coefficient of
variation (CV) of force during the fatiguing contraction (30% MVC) across time for men (closed symbols)
and women (open symbols) during the control session (circle), low-cognitive demand session (Low-CD,
triangle), and high-cognitive demand session (High-CD, square). The CV of torque is shown for the
control (circle), low cognitive demand (triangle), and high cognitive demand (square) sessions for men
(closed symbols) and women (open symbols) during five continuous intervals of 20% during the fatiguing
contraction. The x-axis intervals indicate the last time point of data averaged in that interval (20% on the
axis for example, is equivalent to the 0-20% of the contraction, and the 100% represents 80-100% of the
time to failure). Fluctuations in torque (CV) increased over time across sessions (session × time, p =
0.0001) and women had significantly higher fluctuations in torque than men (sex effect, p = 0.002).
EMG Activity of Agonist Muscles. The amplitude of the RMS EMG (% MVC) for
the tibialis anterior (TA, ankle dorsiflexor muscle), increased during the fatiguing
contraction (time, p < 0.001) similarly across sessions (session × time, p = 0.836) and
between men and women (sex effect, p = 0.882). Bursting activity (bursts per minute)
increased over time during the fatiguing contraction across sessions (time, p = 0.005),
similarly for men and women (sex effect, p = 0.471), with no interactions (p > 0.05).
There was no difference in bursting activity between men and women within sessions (p
> 0.05).
EMG Activity of Antagonist and Accessory Muscles. The RMS EMG amplitude
(% EMG) for the gastrocnemius, soleus, and rectus femoris increased over time during
81
the fatiguing contractions (time effect, p < 0.001 for all muscles) but was similar across
sessions (session effect, p > 0.05 for all muscles) and sex (sex effect, p > 0.05) with no
interactions (session × sex, p > 0.05).
Coactivation ratios for gastrocnemius relative to the tibialis anterior were similar
across sessions (session, p = 0.164), over time (time, p = 0.161), and for men and women
(p = 0.129), with no interactions (p > 0.05). Coactivation ratios for the soleus relative to
the tibialis anterior were also similar across sessions (session, p = 0.125), over time (time,
p = 0.245), for men and women (sex effect, p = 0.977), and there were no interactions (p
> 0.05).
Physiological Response during Fatiguing Contraction. Mean arterial pressure
(MAP) increased over time during the fatiguing contraction (time, p = 0.001), was higher
during the high-cognitive demand session for men than women (session × sex, p =
0.031), but there was no main effect of sex (sex effect, p = 0.170), and there were no
interactions (p > 0.05). Heart rate was significantly higher during the high-cognitive
demand session (session, p = 0.004), over time (time, p = 0.0001), but there was no
difference between men and women (sex effect, p = 0.458) and no interactions (p > 0.05).
Rate-pressure product (RPP) was higher during the high-cognitive demand session
(session, p = 0.014) over time (time effect, p = 0.0001), but there was no difference
between men and women (sex effect, p = 0.856) and no interactions (p > 0.05). Ratings
of Perceived Exertion (RPE) increased during the fatiguing contraction (time effect, p =
0.0001) similarly across sessions (session effect, p = 0.709). RPE was similar for men
and women (sex effect, p = 0.514), with no interactions (p > 0.05).
82
DISCUSSION
This study compared the force fluctuations during very low and moderate
intensity contractions and the time to task failure for a moderate-force fatiguing
contractions of the ankle dorsiflexor muscles in the presence and absence of increased
cognitive demand for young men and women. The novel findings of this study were that
women demonstrated: 1) greater force fluctuations than men during the very low (5%
MVC) force task and the moderate-force (30% MVC) fatiguing contraction regardless of
cognitive demand, and 2) a similar time to task failure as men for the moderate-force task
regardless of cognitive demand. In contrast to the elbow flexor muscles (Keller-Ross,
Pruse, et al., 2014; Yoon et al., 2009), the fatigability of the dorsiflexor muscles in young
men and women was not greater with superimposition of either a high cognitive demand
task, despite elevated levels of reported stress and anxiety. Force fluctuations in the
upright posture however, were greater for women than men for the low and moderate
force contractions.
Force fluctuations at low-to-moderate forces appear to be the result of multiple
factors that influence the output of motoneurones including synaptic noise and the
common input or drive to the motoneurones (Dideriksen et al., 2012; Hamilton et al.,
2004; Jesunathadas et al., 2012; Negro et al., 2009). Synaptic noise in the motoneurone
pool will increase motor unit discharge rates and likely influence the force fluctuations at
the very low forces (Jesunathadas et al., 2012; Moritz et al., 2005). The primary
mechanism for force fluctuations however across the range of low to high forces is the
thought to be due to centrally mediated common drive that oscillates at < 2-3 and
influence the discharge rates of the trains of motoneurones in unison (De Luca et al.,
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2009; De Luca et al., 1982b; Negro et al., 2009). Fluctuations in force at lower force
levels (5% MVC) may also be exacerbated by the use of visual feedback during a
sustained contractions (Tracy, 2007b). Although there is some agreement on the
mechanisms that contribute to force fluctuations during sustained contractions, the
majority of participants in theses previous studies were men providing very little insight
regarding the cause of greater force fluctuations found in women.
Women demonstrated greater force fluctuations during both the low-force (5%
MVC) and moderate-force (30% MVC) contractions with the ankle dorsiflexion. Some
but not all studies have shown these sex differences in the elbow flexor muscles and hip
extensors. For example, Brown, et al (2010) demonstrated that women had overall
decreased steadiness during elbow flexion tasks; women were also less steady than men
while performing a target-matching task of the hip extensors, this was only seen at low
target values (Grunte et al., 2009). Several factors may have influenced the decreased
steadiness found in women in this study. Women have been reported to have lower
motor unit discharge rates and higher discharge rate variability than men during isometric
elbow flexion contractions (Brown et al., 2009). Both of these factors would result in a
significant increase in force fluctuations when compared with men (Moritz et al., 2005);
however, there is very little additional evidence to support this finding in the literature.
Other possible mechanisms for the sex difference in force fluctuations include
muscle strength and simultaneous activation of the antagonist muscles. While others
indicate that coactivation has also been reported as a potential contributor to decreased
steadiness during low-force tasks (Kouzaki et al., 2004), we found were no differences in
coactivation between men and women at either low- or moderate-intensity contractions in
84
this study. Thus coactivation did not appear to explain the sex difference in force
fluctuations.
We found a negative correlation between steadiness and strength during the
control session for the 5% MVC task indicating that stronger adults (men) had a lower
CV. However, there was no relationship between steadiness and strength during the 30%
MVC task. Brown, et al (2010) also reported a strong relationship (r = -0.49) between
increased force fluctuations and decreased strength demonstrated by women compared
with men at all submaximal force levels across three positions of the forearm (neutral,
pronated and supinated). Thus, although women were consistently weaker and less steady
than men during the ankle dorsiflexion task, it is unlikely that strength influenced
steadiness during the moderate-intensity contraction (30% MVC), but it may have been a
factor during the low-intensity contraction (5% MVC). This is consistent with reports in
the literature that stronger joints generate less variable torques and that stronger muscles
produce less force noise due to having higher motor unit numbers and firing rates than
weaker muscles (Hamilton et al., 2004), further potentially causing women with less
strength to perform with greater force fluctuations than men.
In contrast to the elbow flexor muscles and small muscle of the hand, cognitive
demand and increased stress did not increase the force fluctuations during the isometric
contractions (Christou et al., 2004; Keller-Ross, Pruse, et al., 2014; Yoon et al., 2009).
Unexpectedly, women appeared to have improved their steadiness during the high
cognitive demand compared with the control and low cognitive demand, although this did
not reach statistical significance but the men did not. For the control and low-cognitive
demand sessions, fluctuations in force were also negatively correlated with strength, but
85
not during the high-cognitive demand session. As a response to increased acute anxiety,
the sympathetic nervous system may have released neuromodulators that evoked a
response from the motor system or spinal cord that altered recruitment or rate coding of
motor units enabling the participant to perform the motor task with greater steadiness
(Marmon & Enoka, 2010). However, why women appeared to improve steadiness and
men did not, is unclear.
Time to failure of the 30% MVC task did not alter with cognitive demand for both
men and women. These results for the ankle dorsiflexor muscles are in contrast to that
for the elbow flexor muscles (Keller-Ross, Pruse, et al., 2014; Yoon et al., 2009). The
increased fatigability with high cognitive demand was associated with initial strength for
the elbow flexor muscles such that weaker subjects had greater reduction in time to
failure when the cognitive high-cognitive demand was imposed (Keller et al 2014). For
the ankle dorsiflexors, there was no correlation between strength and time to task failure
[as has been seen before (Avin et al., 2010)], nor were there indications of significant
differences in cardiovascular responses between men and women as has been observed
for the elbow flexor muscles. For the elbow flexor muscles, one explanation for the
reduction in time to failure was a strength-related perfusion mechanisms such that for
weaker subjects the perfusion advantage at the start of a contraction (due to less
intramuscular pressure) was altered with increased cognitive demand and arousal (Yoon
et al 2009; Keller et al 2014). For the dorsiflexor muscles, the reduced association
between strength and time to failure (Avin et al., 2010) and the slightly higher intensity
contraction, may have ameliorated any perfusion related mechanism for influencing the
time to task failure.
86
Alternatively, the lack of increase in the force fluctuations and no change in time
to failure with increased cognitive demand for both young men and women may also
have been a consequence of the reduced number of corticospinal inputs to the
motoneurone pool in the lower limb relative to the upper limb resulting in less
modulation at of the motor unit output. Taken together with previous studies (Christou et
al., 2004; Keller-Ross, Pruse, et al., 2014; Yoon et al., 2009), our findings suggest that
because both young men and women demonstrate similar times to task failure and force
fluctuations during the 30% MVC fatiguing task regardless of cognitive demand as well
and similar force fluctuations during a 5% MVC task in the presence of high-cognitive
demand, they are more able to successfully achieve a difficult cognitive task and a target
matching motor task with the lower limb than with the upper limb.
87
CHAPTER IV
Motor variability during sustained contractions increases with cognitive
demand in older adults
SUMMARY
To expose cortical involvement in age-related changes in motor performance, we
compared steadiness (force fluctuations) and fatigability of submaximal isometric
contractions with the ankle dorsiflexor muscles in older and young adults and with
varying levels of cognitive demand imposed. Sixteen young (20.4 ± 2.1 yr: 8 men, 8
women) and 17 older adults (68.8 ± 4.4: 9 men, 8 women) attended three sessions and
performed a 40 s isometric contraction at 5% maximal voluntary contraction (MVC)
force followed by an isometric contraction at 30% MVC until task failure. The cognitive
demand required during the submaximal contractions in each session differed as follows:
1) high-cognitive demand session where difficult mental math was imposed (counting
backward by 13 from a 4-digit number); 2) low-cognitive demand session which involved
simple mental math (counting backward by one); and 3) control session with no mental
math. Anxiety was elevated during the high-cognitive demand session compared with
other sessions for both age groups but more so for the older adults than young adults
(p<0.05). Older adults had larger force fluctuations than young adults during: 1) the 5%
MVC task as cognitive demand increased (p=0.007), and 2) the fatiguing contraction for
all sessions (p=0.002). Time to task failure did not differ between sessions or age groups
(p>0.05), but the variability between sessions (standard deviation [SD] of 3 sessions) was
88
greater for older adults than young (2.02 ± 1.05 min vs. 1.25±0.51 min, P<0.05). Thus,
variability in lower limb motor performance for low- and moderate-force isometric tasks
increased with age and was exacerbated when cognitive demand was imposed during a
very low-force contraction indicating that age-related variability of sustained contractions
can originate from central sources. These data have significant performance implications
for cognitively demanding low-force motor tasks that are relevant to functional and
ergonomic in an aging workforce.
INTRODUCTION
Aging results in marked declines in both motor performance and cognitive
function. For example, older adults are weaker and less steady (i.e., they exhibit greater
fluctuations in force around a target force) than young adults (Enoka et al., 2003; Hunter,
Todd, et al., 2008). Decreased steadiness with age is greatest during low-intensity
isometric contractions in lower and upper extremity muscles (Enoka et al., 2003;
Marmon, Pascoe, et al., 2011; Tracy, 2007a) probably due to age-related changes in
inputs to the motoneurone pool (Barry et al., 2007). Cognitive impairment can also be
marked but subtle and often subclinical in early stages of cognitive dysfunction (Aine et
al., 2011; Chen et al., 2001; Morris et al., 2001); it is often observed as degradation in
short-term memory and executive function resulting in a decreased ability to perform
daily tasks that are dependent upon memory-related abilities (Artero et al., 2001; Farias et
al., 2012; Morris et al., 2001), including planning and decision-making in lower limb
activities such as gait (Yogev-Seligmann et al., 2008). Age-related declines in motor and
cognitive function are usually studied separately but are often performed simultaneously
in daily tasks. The current study assessed motor function with a focus on steadiness and
89
muscle fatigability in young and older adults while they were presented with low and
high levels of cognitive demand.
In young adults, cognitive performance declines and force fluctuations increase
when a cognitive task is imposed (reaction time) during sustained isometric tasks with
hand muscles (Lorist et al., 2002; Zijdewind et al., 2006); however, force fluctuations
were affected more during the isometric fatiguing contractions than during a 5% maximal
voluntary contraction (MVC) submaximal non-fatiguing contraction (Lorist et al., 2002).
We also previously found that steadiness of the elbow flexor muscles declined (increased
force fluctuations) and time to failure of a sustained 20% MVC submaximal task was
reduced (increased fatigability) in young adults when simultaneously performing a
demanding cognitive task (‘high cognitive demand’) that increased anxiety (counting
backwards by 13). Accordingly heart rate and blood pressure, which are indices of
increased sympathetic activity with arousal (Kajantie & Phillips, 2006), were elevated
during the ‘high-cognitive demand’ session compared with control (Keller-Ross, Pruse, et
al., 2014; Yoon et al., 2009). Individuals who were weaker (primarily the women)
showed the largest decrement in time to task failure when the stressful cognitive task was
imposed during the fatiguing contraction (Keller-Ross, Pruse, et al., 2014; Yoon et al.,
2009). Older adults are typically weaker than young adults, and older women being
weaker than older men for upper and lower limb muscles (Galganski et al., 1993; Laidlaw
et al., 2000; Tracy & Enoka, 2002), possibly increasing susceptibility to increased
fatigability when a cognitive task is imposed. However, it is not known whether
fatigability with increased cognitive demand is exacerbated with advanced age.
90
Furthermore, the effects of increased cognitive demand on lower limb fatigability and
steadiness in young or older adults are not known.
Initial evidence would suggest that age-related decrements in motor function of
the upper limb (e.g., reduced steadiness) become larger with greater cortical involvement
of non-motor centers (Fraser et al., 2010; Voelcker-Rehage & Alberts, 2007; VoelckerRehage et al., 2006) and may increase between- and within-participant variability
particularly in older adults (Enoka et al., 2003; Sosnoff & Newell, 2006a). Older adults
display greater between- and within-participant variability than young in activation of
supraspinal centers during maximal contractions with the upper limb (Hunter, Todd, et
al., 2008) indicating that variability within cortical motor areas is larger with advanced
age. Increased cognitive involvement, particularly with tasks that tax attentional
resources and that require the use of short-term memory or executive function, may
contribute further to age-related variability in motor performance for older adults during
functional tasks (Sommervoll et al., 2011; Voelcker-Rehage et al., 2006; YogevSeligmann et al., 2008).
Increased cognitive involvement however, can increase anxiety and stress levels
(e.g., Noteboom, Fleshner, et al., 2001) which may further decrease steadiness of upper
limb muscles in older adults compared with young. Force fluctuations, for example,
increased for older adults more than young when exposed to a noxious stressor
(unpredictable electrical stimulation to the hand) prior to performing the pinch grip task
(Christou et al., 2004). The increased force fluctuations therefore, could be due to agerelated changes in monoaminergic drive modulating inputs to the motoneurone pool
(Barry et al., 2007). A novel aspect of this current study is that we varied the level of
91
cognitive demand administered simultaneously during motor task of the lower limbs to
determine its influence on lower limb fatigability and steadiness and any accompanying
changes in anxiety and stress.
The purpose of this study was to compare both the amplitude of force fluctuations
(steadiness) and time to task failure (fatigability) for low-to-moderate-force isometric
contractions in the presence and absence of varying levels of cognitive demand in young
and older adults. Participants were exposed to two different levels of cognitive load, lowand high-cognitive demand while performing a motor task with the ankle dorsiflexor
muscles, which are muscles that play a functional role controlling the position of the foot
during walking and while maintaining balance. We hypothesized that older adults would
show greater reductions in time to task failure and greater force fluctuations than young
as cognitive demand increased. We also compared variability of fatigability between and
within young and older adults with increased cognitive demand. We hypothesized that as
cognitive demand increased, older adults would exhibit both greater between- and withinparticipant variability in fatigability. To understand the perceived and physiological
arousal responses with the varying levels of cognitive demand in young and older adults,
during each session we quantified perceived levels of stress and anxiety, as well as heart
rate, blood pressure and perceived effort of contraction.
MATERIALS AND METHODS
Sixteen young adults (8 men, 8 women; 18 - 24 years) and 17 older adults (9 men,
8 women; 62 - 79 years) participated in the study (see Table 3.1 for physical
characteristics). All participants were healthy with no known neurological or
cardiovascular diseases, had controlled blood pressure and were naïve to the protocol.
92
Six of the older adults were on medication to control blood pressure. Both young and
older adults had low-to-moderate levels of anxiety (trait) according to the State-Trait
Anxiety Inventory (STAI) (Spielberger, 2010) and reported no history of current mental
or psychological pathology, including anxiety or depressive disorders. Participants were
right-leg dominant (0.81 ± 0.40 vs. 0.80 ± 0.54 for young and older adults, respectively,
with a ratio of 1 indicating complete dominance of the right-leg) (Oldfield, 1971). The
physical activity level for each participant was assessed with a questionnaire that
estimated the relative kilocalorie expenditure of energy per week (Kriska & Bennett,
1992). Prior to participation, each participant provided informed consent, and the
protocol was approved by the Institutional Review Board at Marquette University.
The experimental Materials and Methods for Chapter IV are identical to Chapter
III. To avoid redundancy, all experimental Materials and Methods information has been
removed from Chapter IV. Please refer to Chapter III for experimental procedures,
protocol, data acquisition, analysis information, etc.
Statistical Analysis
Data were reported as means ± SD within the text, and displayed as means ± SE
in the figures. Analyses of variances (ANOVA) models were used to compare the various
dependent variables. Specifically, separate ANOVAs with repeated measures on session
(control, low-cognitive demand and high-cognitive demand), and with age (young and
old) and sex (men and women) as fixed factors, were used to compare MVC torque and
STAI (state) and VAS for anxiety and stress at baseline, MAP, heart rate, CV of torque
and EMG activity during the 5% MVC task, error rates (only low- and high-cognitive
demand sessions included for error rate analysis) during the fatiguing contraction and the
93
time to task failure of the fatiguing contraction. This ANOVA model was also used to
compare the SD of the time to failure from the three sessions, and the SD of the mean CV
of torque during the fatiguing contraction obtained from the three sessions. ANOVAs
with repeated measures on session and time, and with age and sex as fixed factors, were
used to compare VAS for stress and anxiety, STAI (state) and MVC torques throughout
the sessions i.e. before and after the fatiguing contraction (see Figure 3.1 for time points).
ANOVAs with repeated measures on session and time (five time points for each: see data
analysis section), and with age and sex as fixed factors, were used to compare the
following variables during the fatiguing contraction: CV of torque, RMS EMG, EMG
bursting activity, heart rate, MAP, rate pressure product and rating of perceived exertion.
Post hoc analyses (Tukey) were used to test for differences among pairs when
appropriate. Univariate ANOVAs were used to compare young and old men and women
for the following variables: physical characteristics (age, height and weight), physical
activity level, handedness and STAI (trait). The strength of an association is reported as
the Pearson product-moment correlation coefficient (r). The statistical significance
adopted was 5% (p <0.05) and all analysis were performed in IBM Statistical Package for
Social Sciences (SPSS) version 19.
94
Table 4.1. Participant Physical Characteristics and Results. Participant physical characteristics and
age group means (± SD) for control, low-cognitive demand (Low-CD) and high-cognitive demand (HighCD) sessions.
Variable
Session
Young
Older
Number of Participants
Age (years)
Height (cm)
Women Age (years)
Men Age (years)
Weight (kg)
Physical Activity (PAQ)
Baseline Trait STAI Scores
Baseline State STAI Scores
MVC Torque* (Nm)
MVC Torque Recovery
(% of initial)
Time to Task Failure (min)
30% MVC CV of Torque* (%)
TA EMG* (% MVC)
TA EMG Bursting Activity
(bursts/min)
Soleus EMG (% MVC)
Gastrocnemius EMG (% MVC)
Rectus Femoris EMG (% MVC)
Control
Low-CD
High-CD
Total
Control
Low-CD
High-CD
Total
Control
Low-CD
High-CD
Total
Control
Low-CD
High-CD
Total
Control
Low-CD
High-CD
Total
Control
Low-CD
High-CD
Total
Control
Low-CD
High-CD
Total
Control
Low-CD
High-CD
Total
Control
Low-CD
High-CD
Total
16
20.4 ± 2.1
168.9 ± 23.6
19.3 ± 1.5
21.5 ± 2.0
74.4 ± 27.9
59.5 ± 38.3
36.4 ± 7
26.0 ± 6.7
22.4 ± 6.9
22.2 ± 6.6
22.8 ± 7.7
22.5 ± 7.0
96.3 ± 7.4%
95.4 ± 3.7%
94.8 ± 3.9%
96.2 ± 7.4%
6.1 ± 2.2
6.1 ± 2.0
7.1 ± 2.5
6.4 ± 2.2
4.5 ± 1.2
4.5 ± 0.8
4.6 ± 2.7
4.5 ± 1.0
23.5 ± 5.6
23.5 ± 6.2
24.1 ± 4.8
23.7 ± 4.3
10.3 ± 9.7
11.5 ± 10.6
9.4 ± 9.6
10.4 ± 9.8
23.8 ± 38.9
63.0 ± 98.9
65.5 ± 102.3
49.3 ± 80.8
26.7 ± 15.8
26.3 ± 9.9
21.1 ± 6.1
24.2 ± 11.3
22.8 ± 42.2
72.0 ± 124.6
73.3 ± 126.9
40.6 ± 82.1
17
68.8 ± 4.4*
167.5 ± 11.3
68.4 ± 3.6
69.2 ± 4.9
77.8 ± 15.2
22.0 ± 21.8*
34.8 ± 7.3
26.2 ± 5.3
19.1 ± 6.6
18.3 ± 5.8
18.7 ± 6.3
18.7 ± 6.1*
97.9 ± 6.8%
93.2 ± 8.5%
94.7 ± 5.5%
97.6 ± 6.5%
8.0 ± 3.2
7.6 ± 3.1
8.7 ± 3.8
8.1 ± 3.4
6.0 ± 3.0
7.2 ± 4.1
6.8 ± 2.3
6.7 ± 3.2*
29.1 ± 6.0
27.2 ± 4.7
28.7 ± 8.4
28.3 ± 5.9*
5.7 ± 5.7
9.0 ± 9.2
7.7 ± 7.6
7.4 ± 7.6
35.6 ± 61.5
40.1 ± 57.8
56.2 ± 63.5
46.6 ± 58.9
32.9 ± 13.8
30.6 ± 11.8
29.9 ± 13.8
31.1 ± 12.9
30.3 ± 47.9
51.7 ± 83.8
52.0 ± 85.9
46.0 ± 73.8
* Variables reaching statistical significance for main effect of age (p < 0.05). PAQ = Physical Activity
Questionnaire, STAI = State-Trait Anxiety Index, MVC = Maximal Voluntary Contraction, CV =
Coefficient of Variation, TA = Tibialis Anterior, Gastroc = Gastrocnemius, EMG = Electromyography.
95
RESULTS
MVC Torque
At baseline, young adults were stronger than the older adults (22.5 ± 7 Nm vs.
18.7 ± 6.1 Nm respectively: age effect, p = 0.005) and men were stronger than women
(sex effect, p = 0.0001) on all three days of testing, with no interaction between sex and
age (age × sex, p = 0.69; Table 4.1). The relative reduction (%) in MVC torque after the
fatiguing contraction was similar across sessions (session effect, p = 0.98), and similar for
young and older adults (session × age, p = 0.26). During recovery, MVC torque
increased to near baseline levels within 10 minutes of completing the fatiguing
contraction similarly for young and older adults across all sessions (session × age, p =
0.57; Table 4.1). Furthermore, at the end of the recovery period, the MVC (% of
baseline) was similar for young and older men and women (p > 0.05).
Anxiety and Stress Levels
State STAI Scores. Baseline state STAI scores taken at the beginning of each
experimental session were similar for young and older men and women (age effect, p =
0.88; sex effect, p = 0.57; Table 4.1). State STAI scores taken after exposure to the 2 × 2
min of difficult mental math were higher during the high-cognitive demand session
compared with the control and low-cognitive demand sessions (control, 25.1 ± 6.6; lowcognitive demand, 30.4 ± 10.0; high-cognitive demand, 42.8 ± 13.1; session effect, p =
0.0001) and immediately after the fatiguing contraction (control, 36.2 ± 9.2; lowcognitive demand, 38 ± 9; high-cognitive demand , 50.4 ± 13.8; session effect, p =
0.0001). There was no difference between young and older adults across all three session
96
after completing the fatiguing contraction (age effect, p = 0.72). Older adults however,
demonstrated higher state STAI scores than young adults after the fatiguing contraction
when exposed to the high-cognitive demand (session × time × age, p = 0.02).
VAS for Stress and Anxiety. VAS scores for stress and anxiety were similar at
baseline for young and older adults, and men and women (p > 0.05; Figure 4.1). Anxiety
VAS was significantly higher during the high-cognitive demand session compared to the
control and low-cognitive demand sessions (session × time, p = 0.0001), and increased
more for older adults than young over time (time × age, p = 0.01; Figure 4.1A). There
were no other interactions. Stress VAS scores were significantly higher for older adults
than young during the high-cognitive demand session compared with the control and lowcognitive demand sessions (session × age, p = 0.02; Figure 4.1B) and for older adults
than young over time (time × age, p = 0.001). There were no main effects of sex and no
other interactions.
97
A
Young Control
Old Control
Young Low-CD
Old Low-CD
Young High-CD
Old High-CD
Anxiety VAS (cm)
6
4
2
0
Baseline
Cognitive
Demand
T1
T3
T2
T4
Recovery
T5
T6
T7
Time
B
Stress VAS (cm)
6
4
2
0
Baseline Cognitive
Demand
T1
T2
T3
Recovery
T4 T5
Time
T6
T7
Figure 4.1. Visual Analogue Scale (VAS) scores for anxiety (A) and stress (B). Mean (±SE) VAS
scores for young adults (closed symbols) and older adults (open symbols) are shown for anxiety (A)
and stress (B) throughout the experimental protocol during the control session (circles), lowcognitive demand session (Low-CD, triangles), and high-cognitive demand session (High-CD,
squares). Time intervals were as follows: baseline (T1), after the first bout of 2 min of quiet
rest/mental math (T2), after the second bout of 2 min of quiet rest/mental math (T3), after the 5%
submaximal contraction (T4), during recovery immediately after task failure (T5), and then at 5 min
(T6) and 10 min of recovery (T7).
98
Low-Intensity Sustained Contraction (5% MVC)
Fluctuations in Torque. Older adults had greater fluctuations in torque
(coefficient of variation of force: CV) compared with young across all three sessions (age
effect, p = 0.007; Figure 4.2) and women had higher fluctuations in torque than men (sex
effect, p = 0.001). On average, the older adults had a linear increase in fluctuations in
torque as cognitive demand increased while young adults showed no change (linear
interaction for session × age, p = 0.04; Figure 4.2A). Women demonstrated higher force
fluctuations than men (p = 0.001), but there were no other interactions (p > 0.05).
99
A
20
Young
Old
CV of Torque (%)
18
16
14
12
10
8
6
Control
Low-CD
High-CD
Session
Figure 4.2. Mean Session Coefficient of Variation (CV) of torque during the 5% MVC task
for young (closed symbols) and older (open symbols) adults during the control, lowcognitive demand (Low-CD), and high-cognitive demand (High-CD) sessions (A), and
representative force tracings of a young and older adult (B). Older adults had significantly
higher CV of torque than young adults (age effect, p = 0.007).
100
MAP and Heart Rate. Cardiovascular measures were analyzed for only those
older participants who were not currently taking blood pressure medications at the time
of the experiment (young, n = 16; older, n = 11). During the 5% MVC task, MAP was
higher during the high-cognitive demand session (105.1 ± 18.8 mmHg) than the lowcognitive demand session (87.2 ± 28.3 mmHg) and the control session (88.8 ± 23 mmHg;
session effect, p = 0.001). There was no influence of age or sex on MAP (p > 0.05).
Similarly, heart rate was greater during the high-cognitive demand session (79.6 ± 15
beats.min-1) compared with the low-cognitive demand (71.6 ± 28 beats.min-1) and control
sessions (68.2 ± 19.4 beats.min-1, session effect, p = 0.04) regardless of age of sex (p >
0.05, i.e. no interactions). Consequently, the rate pressure product was higher during the
high-cognitive demand session than the control and low-cognitive demand sessions
(session effect, p = 0.0001), but there was no difference between young and older adults
(age effect, p = 0.52) or men and women (sex effect, p = 0.70).
EMG Activity. During the 5% MVC task, older adults had higher soleus RMS
EMG (% MVC) (antagonist muscle) than young adults during the high-cognitive demand
session (session × age, p = 0.04), but there were no other interactions, or main effects of
age or sex or session for soleus or any other muscles (tibialis anterior, gastrocnemius, and
rectus femoris).
Fatiguing Contraction (30%MVC)
Time to Task Failure. There was no difference in time to task failure across
sessions or with age (p > 0.05; Table 4.1 and Figure 4.3). There was no difference in
time to task failure between men and women (sex effect, p = 0.96) and no interactions for
age, sex and session (p > 0.05). Variability between the three sessions in the time to task
101
failure (comparison of standard deviation [SD] generated from the three sessions for each
participant) however, was greater for older adults than young adults (2.02 ± 1.05 min vs.
1.25 ± 0.51 min respectively, p = 0.02), but similar for men and women (sex effect, p =
0.53; sex × age, p = 0.57; Figure 4.3). Furthermore, we compared the SD for time to task
failure between sessions (SD for control and low-cognitive demand session versus SD for
control and high-cognitive demand session) to determine if variability increased with
difficulty of the mental math. While the age effect remained (age effect, p = 0.03), there
were no effects of session (session effect, p = 0.38) or sex (sex effect, p = 0.69), and no
interactions. Thus, although the older adults were more variable than young between the
three sessions due to addition of a cognitive task, the variability between the older adults
did not increase with to the difficulty of the cognitive task.
102
Young
Time to Failure (min)
A
25
Men
Women
(n = 8)
(n = 8)
Control
Low-CD
High-CD
20
15
10
5
0
Mean
Subject
Old
Time to Failure (min)
B
25
Men
Women
(n = 9)
(n = 8)
20
15
10
5
0
Subject
Mean
Figure 4.3. Time to task failure during the fatiguing contraction for individual young (A)
and older adults (B) during the control (circle), low-cognitive demand (Low-CD, triangle),
and high-cognitive demand (High-CD, square) sessions. Time to task failure is shown for
each young (A) and older (B) man and woman for each session (separated by dashed line). The
aggregate mean (±SE) is shown for each session after the solid vertical line. The range and
variability of time to task failure among the older adults was greater than for the young adults for
each session. Older adults were more variable than young between the three sessions due to
addition of a cognitive task (age effect, p = 0.03), but variability did not increase with task
difficulty (session effect, p = 0.38).
103
Fluctuations in Torque. Fluctuations in torque (CV) increased over time during
the sustained contraction (time, p < 0.001) similarly across sessions for both age groups
(session x time, p = 0.56; Figure 4.4C). Older adults however, had larger fluctuations in
torque than young adults (age effect, p = 0.002) and this difference was similar across
sessions (session × age, p = 0.11). Women also had larger fluctuations in torque than men
(sex effect, p = 0.005) and this sex difference was similar across sessions (session × sex,
p = 0.69). There were no other interactions (p > 0.05). Because older and young adults
can demonstrate more variability in motor performance than young adults, we evaluated
variability of the fluctuations in torque (CV of torque) during the 30% MVC task
(comparison of standard deviation [SD] generated from the three sessions for each
participant) between young and older adults. Variability was greater for older adults than
young adults (p = 0.01), and greater for women than men (sex effect, p = 0.02), but there
were no interactions (sex × age, p = 0.94; Figure 4.4A-B).
104
Young
CV of Torque (%)
A
Old
Men
Women
(n = 8)
(n = 8)
20
B
20
15
15
10
10
5
5
Subject
C
Mean
Men
Women
(n = 9)
(n = 8)
Subject
Control
Low-CD
High-CD
Mean
CV of Torque (%)
10
Young Control
Old Control
Young Low-CD
Old Low-CD
Young High-CD
Old High-CD
8
6
4
2
0
0
0
0
0
0
0-2 20-4 40-6 60-8 0-10
8
Time (%)
Figure 4.4. Coefficient of Variation of Force during Fatiguing Contraction. Coefficient of variation
(CV) of force during the fatiguing contraction (30% MVC) for individual young (A) and older adults (B),
and across time for young and older adults (C) during the control session (circle), low-cognitive demand
session (Low-CD, triangle), and high-cognitive demand session (High-CD, square). CV of force is shown
for each young (A) and older (B) man and woman for each session. The aggregate mean is shown for each
session after the solid vertical line. In panel (C), the CV of torque is shown for the control (circle), lowcognitive demand (triangle), and high-cognitive demand (square) sessions for young (closed symbols) and
older (open symbols) adults during five continuous intervals of 20% during the fatiguing contraction. The
x-axis intervals for panel (C) indicate the last time point of data average along in that interval (20% on the
axis for example, is equivalent to the 0-20% of the contraction, and the 100% represents 80-100% of the
time to failure). Older adults had significantly higher CV of torque than young adults (age effect, p =
0.002).
105
EMG Activity of Agonist Muscles. The amplitude of the RMS EMG (% MVC) for
the tibialis anterior (ankle dorsiflexors), increased during the fatiguing contraction (time
effect, p < 0.001) and similarly across sessions (session × time, p = 0.38). Furthermore,
tibialis anterior EMG activity was greater for the older adults than young during all three
sessions (age effect, p = 0.002; Table 4.1), but there was no difference between men and
women (sex effect, p = 0.80), and no interactions.
EMG bursting activity (bursts per minute) increased over time during the
fatiguing contraction (30% MVC) for all three sessions (time effect, p = 0.002); however,
there was no difference across the sessions (session effect, p = 0.50). Neither age nor sex
influenced the bursting activity during the fatiguing contraction (p > 0.05; Table 4.1).
EMG Activity of Antagonist and Accessory Muscles. The RMS EMG amplitude
(% MVC) for the soleus, gastrocnemius and rectus femoris increased over time during the
fatiguing contractions (time effect, p < 0.001 for all muscles) but was similar across
sessions (session effect, p > 0.05 for all muscles). Older adults however, had higher
RMS EMG for the soleus (age effect, p = 0.04), gastrocnemius (p = 0.01) and rectus
femoris (p = 0.03) than young adults. There was no effect of sex for gastrocnemius and
soleus (sex effect, p > 0.05) and there were no interactions (p > 0.05). For the rectus
femoris, however, women had higher RMS EMG amplitudes than men (sex effect, p =
0.04), but there were no interactions (p > 0.05). There was a correlation between rectus
femoris RMS EMG and CV of torque for the high-cognitive demand session only,
indicating that participants who had greater torque fluctuations during the high-cognitive
demand session also had greater rectus femoris EMG (r33 = 0.31, p = 0.04).
106
MAP and Heart Rate. Cardiovascular measures were included only for older
participants who were not currently taking blood pressure medications at the time of the
experiment (young, n = 16; older, n = 11). MAP, heart rate, and rate pressure product
increased over time (time, p < 0.05; Figure 4.5). MAP, heart rate and rate pressure
product were higher during the high-cognitive demand session than the control or lowcognitive demand sessions (session effect, p < 0.05). MAP was higher for older adults
during the fatiguing contraction in the high-cognitive demand session over time than
other sessions (session × time × age, p = 0.02). Heart rate and rate pressure product,
however, were similar for older adults over time across sessions (session × time × age, p
> 0.05), with no main effects of age or sex (p > 0.05).
107
A
140
Young Control
Old Control
Young Low-CD
Old High-CD
Young High-CD
Old High-CD
MAP (mmHg)
130
120
110
100
90
80
0
B
25
50
75
Time (%)
100
130
Heart Rate (b.min-1)
120
110
100
90
80
70
60
50
0
Rate Pressure Product (x10 3)
C
25
50
75
100
Time to Failure (%)
13
12
11
10
9
8
7
6
0
25
50
75
100
Time (%)
Figure 4.5. Mean Arterial Pressure (MAP), Heart Rate and Rate Pressure Product. Mean arterial
pressure, heart rate and rate pressure product during the fatiguing contraction for young and older adults
across sessions. The values are mean ± SE at 25% increments of time to task failure for young (closed
symbols) and older (open symbols) adults during the control (circles), low-cognitive demand (Low-CD,
triangles) and high-cognitive demand (High-CD, squares) sessions for mean arterial pressure (A), heart rate
(B), and rate pressure product (C) during the fatiguing contraction. Averages of 15-s intervals were used
for the MAP and heart rate. Rate-pressure product was the product of heart rate and MAP for the
equivalent time periods in panels (A) and (B).
108
Rating of Perceived Exertion (RPE). Perceived exertion increased during the
fatiguing contraction (time effect, p = 0.0001) similarly across sessions (session effect, p
= 0.59). RPE was similar for young and older adults (age effect, p = 0.82) and men and
women (sex effect, p = 0.62), with no interactions (p > 0.05). Mean RPE across all three
sessions was 4.2 ± 1.5 and 4.2 ± 1.8 at the beginning of the fatiguing contraction for
young and older adults respectively and increased to 8.7 ± 2.3 vs. 9.1 ± 1.4 by the end of
the fatiguing contraction.
Error rate. The mental math error rate during the fatiguing contraction
(errors/min) was significantly higher during the high-cognitive demand session (2.9 ± 1.3
errors/min) compared with the low-cognitive demand session (0.4 ± 0.4 errors/min,
session effect, p < 0.001). There was no main effect of age or sex (p > 0.05). There was
also no correlation between error rate and CV of torque during the fatiguing contraction
for the low-cognitive demand (r33 = -0.17, p = 0.35) or high-cognitive demand (r33 = 0.26, p = 0.14) sessions; nor were there significant associations between error rate during
the fatiguing contraction and the time to task failure for the low-cognitive demand (r33 = 0.12, p = 0.52) or high-cognitive demand (r33 = -0.06, p = 0.74) sessions.
DISCUSSION
This study imposed several levels of cognitive demand during sustained low- and
moderate-force isometric contractions with the ankle dorsiflexor muscles to determine the
influence of increased cortical involvement on motor function and fatigability in young
and older adults. The novel findings of this study were that as cognitive demand
increased, steadiness decreased (i.e. CV of torque increased) during the very low-force
contraction (5% MVC) for older adults but did not change for the young adults. While
109
fatigability (time to failure) of a moderate intensity contraction (30% MVC) was not
essentially different with imposed cognitive demand for young or older adults, variability
in the time to failure and in the torque fluctuations across sessions was greater for the
older adults than the young adults. These results provide evidence that increased cortical
involvement of motor and non-motor cortical areas can disrupt motor performance of
low-to-moderate intensity isometric contractions of the lower limb more so in older
adults than young adults.
Steadiness was Reduced with Age in the Lower Limb
Torque fluctuations (CV) were greater (steadiness reduced) during the lowintensity contraction (5% MVC) than at the start of the 30% MVC task (prior to fatigue),
and also greater for older adults than young across all sessions. Larger torque
fluctuations with advanced age have also been observed under control conditions across
various muscle groups and particularly at the lower intensity contractions for both young
and older men and women (Enoka et al., 2003; Tracy, Dinenno, et al., 2007; Tracy et al.,
2005). Typically, the CV (%) will decrease as contraction intensity increases (Enoka et
al., 2003; Moritz et al., 2005; Taylor, A. M. et al., 2003; Tracy, 2007a) and as we
observed. Because we showed increased torque fluctuations during ankle dorsiflexion for
the older adults during both 5% MVC and 30% MVC tasks compared with young, the
age-related mechanism for reduced steadiness under control conditions influences both
the low- and moderate-force tasks in the ankle dorsiflexor muscles. CV of torque across
a range of low-to-high forces appear to be primarily modulated by low-frequency
oscillations (< 2 -3 Hz) in neural drive found in motor unit action potentials trains
(Dideriksen et al., 2012; Negro et al., 2009) with some contribution from increased motor
110
unit variability at very low forces (Dideriksen et al., 2012; Jesunathadas et al., 2012).
This low-frequency oscillating neural drive likely reflects an integration of both
descending and afferent inputs onto the motoneurone pool (Dideriksen et al., 2012;
Farina et al., 2012; Negro et al., 2009). With advanced age, the motoneurone pool
undergoes remodeling that results in decreased motor units numbers and altered relations
between discharge rates and recruitment thresholds (Barry et al., 2007); the age
difference in torque fluctuations, therefore, appears to be due to age-related changes in
the inputs to the motoneurone pool (Barry et al., 2007) with possibly some influence of
greater motor unit discharge rate variability in older adults (Barry et al., 2007; Kornatz et
al., 2005; Laidlaw et al., 2000; Tracy et al., 2005). Age-related changes in visual-motor
processing may also contribute to altered motoneuronal inputs causing increased torque
fluctuations during static contractions with age (Fox et al., 2013; Henningsen et al., 1997;
Seidler-Dobrin & Stelmach, 1998; Tracy, Dinenno, et al., 2007).
Cardiovascular Responses and Anxiety were Elevated with High Cognitive Demand
While mental math was used to manipulate different levels of cognitive demand
in this study, it can also increase anxiety and stress (Kajantie & Phillips, 2006) as it did
for the young but more so in the older adults during the high-cognitive demand task (see
Figure 4.1). Accordingly, MAP and heart rate were elevated when the difficult mental
math was performed during both the 5% MVC and 30% MVC tasks, although similarly
for the young and older adults. Older adults have reduced maximal heart rates compared
with young, in less than for young, explaining the similar age-related increase in heart
rate despite older adults reporting they felt more anxious and stressed. Because both
MAP and heart rate were elevated, rate-pressure product was elevated for both young and
111
older adults indicating increased cardiac work and myocardial oxygen consumption
(Gobel et al., 1978; Wasmund et al., 2002) during the 5% MVC task and 30% MVC
fatiguing contraction when cognitive demand was high. Chronicity of high blood
pressure has been associated with an increased risk of stroke, cognitive decline and
dementia, especially in older adults with untreated high blood pressure (Tzourio,
Christophe 2007; Tzourio, Christophe et al., 1999). In the short term, increased stress and
anxiety can increase monoaminergic drive; neuromodulatory inputs alter excitability of
the motoneurone pool (Heckman et al., 2009) and potentially alter motor neuron output
especially at low forces.
Steadiness Decreased with Cognitive Demand in Older Adults
A novel finding was that the age difference in CV of torque grew linearly (i.e.
steadiness decreased) with the increased levels of cognitive demand during a lowintensity contraction (5% MVC) of the lower limb. Although steadiness during the 30%
MVC task was similar across sessions, older adults had greater variability in the CV of
torque between sessions than the young. Increased torque fluctuations during the 5%
MVC task between the low- and high-cognitive demand sessions indicate that the decline
in steadiness was not solely due to the added distraction or challenge of talking because
the low-cognitive demand task controlled for those factors. Because motor unit discharge
rate variability can contribute to the force fluctuations at very low forces (Jesunathadas et
al., 2012; Tracy et al., 2005), increased variability of the motor unit pool in the older
adults may have been altered when high-cognitive demand was imposed.
Our findings indicate that increased antagonist muscle activation may also have
contributed to the larger force fluctuations in the older adults when cognitive demand was
112
high during the 5% MVC task. The older adults had greater soleus muscle activation
during the high-cognitive demand session relative to the other sessions and compared
with the young adults: this suggests less inhibition of the antagonist muscle from
descending cortical sources. Increased antagonist muscle activity is a strategy adopted by
older adults to stiffen joints and reduce movement variability with age (Hortobagyi &
DeVita, 2006). For the higher force task (30% MVC) the torque fluctuations were larger
with age, but there was no increase in the CV of torque with cognitive demand for young
or older adults. Agonist (tibialis anterior), antagonist (gastrocnemius, soleus) and
synergist (rectus femoris) activations were greater in the older adults than the young
across all sessions, possibly contributing to the larger torque fluctuations with age.
Because both the agonist (tibialis anterior) and antagonist activation were greater in the
older adults compared with the young, activation differences had minimal effect on the
greater CV of torque in the older adults.
There are numerous age-related changes along the neuraxis that can alter inputs to
the motoneurone pool and perhaps make it more susceptible to altering motor output
when cognitive demand is imposed. Cortical size (Raz et al., 2007) and processing are
diminished with age, along with reduced corticospinal fibers numbers (Eisen et al., 1996),
and changes in spinal reflex pathways (Kido et al., 2004) which result in decreased
cortical inhibition of both cognitive and motor processes with age (Hunter, Todd, et al.,
2008; Peinemann et al., 2001; Sale & Semmler, 2005). Age-related changes along the
neuraxis can also result in increased activation of antagonist muscles (Hortobagyi &
DeVita, 2006; Macaluso et al., 2002) and may have been responsible for the greater
activation of antagonist muscles with age in the current study.
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Several theories of motor control assert that the division of attentional resources
during a dual-task paradigm has limits and these limitations increase with advanced aging
due to diminished cortical processing (Woollacott & Shumway-Cook, 2002).
Consequently some studies show older adults have less ability to simultaneously perform
a cognitive task and motor task as well as they can be performed individually (Fraser et
al., 2010; Johnson & Shinohara, 2012; Voelcker-Rehage & Alberts, 2007). Changes in
performance for older adults in dual-tasks appear to be especially sensitive to cognitive
tasks that require executive function (Yogev-Seligmann et al., 2008). Executive function
(which included working memory, which was varied in this study), anxiety, and stress are
modulated in prefrontal cortical regions and the anterior cingulate cortex (Banich et al.,
2009; Miller, 2000; Owen et al., 2005; Schweizer et al., 2013). Prefrontal connections to
motor areas (Takahara et al., 2012) along with input to neural connections between these
and other cortical centers associated with cognition, anxiety and motor function could
directly alter motor output, as was observed in this study.
Capacity theories of attention that assume attentional resource limitations on the
ability to perform multiple tasks simultaneously (Hiraga et al., 2009; Kahneman, 1973;
McDowd, 2007) would predict even greater decrements in steadiness for older adults
when descending drive from the motor cortex increased during the 30% MVC task and as
the fatiguing contraction progressed. Interestingly, error rates in mental math (executive
function task) during the fatiguing contraction did not differ across the age groups for the
low-cognitive demand sessions and high-cognitive demand, indicating that mental math
performance was not diminished in the older adults compared with the young. Although
the variability in the CV of torque across the sessions was greater for the older adults than
114
the young, the mean values in CV of torque were similar across the three sessions during
the 30% MVC task and increased at similar rates to the young adults during the fatiguing
contraction. Time to failure was also similar across sessions for each age group, and the
increase in EMG activity, EMG bursting activity and perceived effort during the
fatiguing contraction progressed at similar rates across sessions. Increases in EMG and
RPE during a fatiguing contraction are the result of increased descending drive to recruit
more motor units in an effort to maintain the required force as the working muscle
becomes progressively fatigued (Riley et al., 2008). Thus, while some older adults are
clearly more affected than others by the increased cognitive demand during the sustained
contractions (Figure 4.4B), capacity limitations in cortical regions of older adults cannot
alone explain the loss of steadiness, especially at the very low forces when descending
drive was not large.
Another explanation for the reduced steadiness in older adults as cognitive
demand increased is that descending and afferent inputs to the motoneurone pool differed
for the older and young adults. One input that likely differed between the young and
older adults was monoaminergic drive (Christou et al., 2004). Increased monoaminergic
drive to the spinal cord from the brainstem enables motoneurone activation and is
essential for exercise (Heckman, 2003), but monoaminergic drive is attenuated in older
adults (Meltzer et al., 1998; Reynolds & Meltzer, 1999; Seals & Esler, 2000), potentially
leading to decreased motor output and altered responses to increased anxiety and stress
compared with young.
115
Sex Differences in Steadiness
Both young and old women demonstrated heightened levels of stress and anxiety,
and greater torque fluctuations during the very low-intensity and fatiguing contractions
for all three sessions compared with men, regardless of the magnitude of cognitive
demand. Similar sex differences in stress and anxiety (Christou et al., 2004) and in torque
fluctuations have been shown previously in the upper limb (Brown et al., 2010; KellerRoss, Pruse, et al., 2014; Yoon et al., 2009); however, this is the first study to
demonstrate increased torque fluctuations in women when performing submaximal
contractions of the lower limb with varying levels of cognitive demand. Greater torque
fluctuations in women when performing upper extremity tasks have been attributed to
strength difference between men and women (Brown et al., 2010), although the
mechanism is not known. When exposed to a stressful noxious stimulus prior to task
performance, increased torque fluctuations have been attributed to greater activation of
central neural mechanisms in response to increased stress and anxiety (Christou et al.,
2004); however, in the current study, women demonstrated greater torque fluctuations
and reported higher stress and anxiety than men regardless of the magnitude of cognitive
demand (i.e. in both low- and high-cognitive demand tasks compared with control).
Because both aging and decreased levels of estrogen in postmenopausal women possibly
contribute to changes in monoaminergic drive (Meltzer et al., 1998), women may be even
more vulnerable than men to diminished motor output with aging (Figure 4.4B);
however, it remains unclear if greater torque fluctuations in women are related to
activation of alternate neural pathways, or a strength-related mechanism.
116
Increased Variability in Fatigability with Cognitive Demand and Aging
Fatigability (time to task failure) of the ankle dorsiflexor muscles was similar
across age groups and sessions. Hence, there was no systematic decrease in fatigability
when cognitive demand was imposed during the sustained contraction with the ankle
dorsiflexor muscles. In contrast, the elbow flexor muscles were more fatigable when
high-cognitive demand was imposed in young men and women (Keller-Ross, Pruse, et
al., 2014; Yoon et al., 2009). Similarly, handgrip muscles were more fatigable in both
young men and women when high-cognitive demand was imposed for high intensity
contractions (Bray et al., 2012; Bray et al., 2008) but not for relatively strong men during
a low-intensity sustained contraction (Keller-Ross, Schlinder-Delap, et al., 2014). The
largest increases in fatigability were related to the initial muscle strength such that
weaker participants experienced the greatest increases in fatigability (Keller-Ross, Pruse,
et al., 2014; Yoon et al., 2009). Perfusion associated changes within the muscle in
response to a mental-math task (which was used to induce high-cognitive demand) was
implicated but only partially explains these findings (Keller-Ross, Pruse, et al., 2014;
Yoon et al., 2009). In contrast to the elbow flexor muscles (Hunter, Critchlow, & Enoka,
2004), the ankle dorsiflexor muscles exhibit lesser differences between sub-populations
including men and women (Avin et al., 2010), and young and older adults (Griffith et al.,
2010; Kent-Braun et al., 2002) and we show here the fatigability of this muscle group is
also less responsive to cognitive demand. Christie and Kamen (2009) attribute the lack
of difference in fatigability of the dorsiflexor muscles between young and older adults to
a lack of difference in motor unit discharge rates, suggesting young and older adults
adopted similar neural adaptations during the fatiguing contractions. We found that the
117
increase in EMG activity of the tibialis anterior muscle during the fatiguing contraction
was similar across sessions for young and older adults, although older adults had greater
EMG relative to the young. Another possible explanation for the different responses in
fatigability with and without cognitive demand is a decreased number of corticospinal
connections and larger motor unit ratio (motoneurone to fibers) in large lower limb
muscles compared with the upper limb (Feinstein et al., 1955). A reduced number of
corticomotor inputs to the dorsiflexor muscles relative to the upper limb muscles may
minimize the modulating inputs from higher centers imposed with high-cognitive demand
and lessen the responsiveness during the sustained fatiguing contractions at the moderate
intensity.
While there was no systematic reduction in time to failure of the ankle dorsiflexor
muscles as we have observed with the upper limb, older adults, particularly older women,
demonstrated significantly more variability in their time to task failure between sessions
than young adults (Figure 4.3). Variability between trials of a motor task can be
exacerbated with increased cognitive demand in young people (Lorist et al., 2002) but is
often greater with age as shown here. The greater variability in performance with
advanced age can be due greater variability in cortical and motor nerve activation during
motor tasks (Hunter, Todd, et al., 2008; Yoon et al., 2008). This greater age-related
variability in a motor task when a cognitive task was imposed further demonstrates the
important role of cognitive control in determining reliability of performance of motor
tasks such as those in the work force.
118
Conclusion
This study demonstrated that older adults exhibit more variability than young
adults in fatigability and less steadiness while performing low-force and moderate
isometric with the ankle dorsiflexor muscles. For very low-force contractions, steadiness
decreased further as greater cognitive demand increased. The reduced steadiness in older
adults compared with the young, may be related to modulation of synergist and
antagonist muscles and an altered neural strategy with age. Older adults also exhibited
greater variability in steadiness between sessions and in fatigability as cognitive demand
was imposed. Increased variability in lower extremity tasks may negatively impact
activities of daily living and work tasks that require high-cognitive demand in an aging
population. These data also expose differences within an older adult but also between
older adults. Our results provide evidence that increased involvement of non-motor
cortical areas can disrupt motor performance of low-to-moderate intensity isometric
contractions of the lower limb more so in older adults than young adults. These findings
have significant implications related to successful aging and performance of activities of
daily living with advanced age especially those activities that require simultaneous
execution of a cognitive task that involves working memory and maintenance of a static
motor task.
119
CHAPTER V
Discussion
This dissertation highlights the importance of cortical involvement in lower limb
motor function, in particular during low-to-moderate force static contractions. Despite the
critical role of force production in the lower limb to perform functional tasks, such as
walking, studies investigating the role of the cortex in force production have been
predominantly performed on the upper extremity resulting in a lack of information
regarding the role of the cortex in the lower limb. This dissertation first investigated the
cortical activation during static contractions of the lower limb muscles in young men and
women using fMRI (study 1, aim 1). This study was then followed by determining the
influence of cortical inputs on force control in young and old men and women,
particularly force fluctuations and fatigability (time to task failure), in the lower limb
during static contractions by manipulating factors that potentially alter cortical inputs
from including force intensity and cognitive demand (study 2 and study 3, aim 2).
One of the first novel findings of this dissertation was that several motor and
sensory areas of the cortex scaled linearly with increased torque (primary motor and
sensory cortices, basal ganglia and cerebellum) (study 1, aim 1). Although lower limb
muscle control during static contractions is different from the upper limb (eg.,
Jesunathadas et al., 2012) and lower limb muscles have fewer direct corticospinal
connections (Brouwer & Ashby, 1990) than upper limb muscles, the active areas in the
cortex associated with lower limb muscles were not unexpected because previous studies
120
have demonstrated similar areas of activation during active ankle movements (Ciccarelli
et al., 2005; Trinastic et al., 2014). Previous studies have also demonstrated scaled
cortical activity with increased force production (in volume, intensity or both) in the
upper limb (Spraker et al., 2007; Thickbroom et al., 1998; Vaillancourt et al., 2004;
Vaillancourt et al., 2007), however this is the first study to demonstrate similar scaling
with force in the lower limb.
Furthermore, cortical areas that are important to modulation of steadiness were
identified. Study 1 determined that there are several motor (basal ganglia, cerebellum,
M1/SMA, and insula) and typically non-motor (superior frontal gyrus, cingulate cortex)
areas that increased activation with increased force fluctuations during high intensity
contractions of the ankle dorsiflexors. The amplitude of the force fluctuations (SD of
torque) increased with force intensity due to activation of more motor units (Laidlaw et
al., 2000) and corresponded with motor areas that increased activation intensity between
low and high forces. More importantly, the results also indicated that other areas of the
cortex (putamen, insula, contralateral superior frontal gyrus and ipsilateral inferior lobes)
were correlated with control of steadiness of the lower limb during target matching
contractions. CV of force (amplitude of the fluctuations normalized to the mean torque)
does not increase with force intensity and is usually largest at lower intensities of
contraction [e.g. (Jesunathadas et al., 2012; Moritz et al., 2005; Taylor, A. M. et al., 2003;
Tracy, Mehoudar, et al., 2007)]. During the 10% MVC task the CV was largest and both
the left superior frontal gyrus motor region and the ipsilateral inferior lobes showed the
largest correlations between CV of force and cortical activation. The CV of force is
thought to be mostly mediated by low-frequency oscillations in neural drive (< 2 -3 Hz)
121
during isometric contractions (Dideriksen et al., 2012; Negro et al., 2009) across a range
of forces. This data raises possibility that these motor regions, particularly the putamen
and the superior frontal gyrus, both of which are associated with limb movement
regulation (Ciccarelli et al., 2005) and force production control (Kuhtz-Buschbeck et al.,
2008) and showed the largest correlations between CV of force and activation for the
10% MVC task, which is the intensity that CV was largest across the intensities, may be
the sources of the low-frequency oscillating neural drive that influences action potentials
trains and ultimately the CV of force.
Cognitive demand however exacerbated the age-related increases in force
fluctuations during the light load contractions. Contrary to the young adults, older adults
demonstrated decreased steadiness while performing a low-force (5% MVC) ankle
dorsiflexion contraction with increased cognitive demand (study 3, aim 2). Increased
force fluctuations during the 5% MVC contraction in the presence of increased cognitive
demand in older adults appears to be the result of two age-related consequences that
occur with normal aging in the neuromuscular system and cognitive processes. The
extensive alterations in the neuromuscular system with aging (Doherty, 2003; Plow et al.,
2013; Semmler et al., 2006) includes altered relations between discharge rates and
recruitment thresholds (Barry et al., 2007), as well as increased in motor unit discharge
rate variability (Barry et al., 2007; Kornatz et al., 2005; Laidlaw et al., 2000; Tracy et al.,
2005), resulting in increased force fluctuations. Similar age-related changes appear to
occur in the prefrontal cortex (Hedden & Gabrieli, 2004, 2005), making cognitive tasks
that demand executive function (Miller & Cohen, 2001; West, 1996) and neural
connectivity from the prefrontal cortex to other essential cortical regions during executive
122
function tasks (Madden et al., 2010) particularly vulnerable with age. Attentional
resource limitations alter the older adult’s ability to perform multiple tasks (motor and
cognitive tasks) simultaneously (Hiraga et al., 2009; Kahneman, 1973; McDowd, 2007),
subsequently resulting in diminished performance in either one or both of the tasks
(Tombu & Jolicoeur, 2003; Yogev-Seligmann et al., 2008). This could be particularly
disruptive when trying to perform a low-force functional task like accelerating or
decelerating a car while talking or answering a question.
Unlike the elbow flexor muscles (Yoon et al 2009, Keller-Ross 2014) fatigability
was not increased for the ankle dorsiflexor muscles for young or older adults. Muscle
fatigue is specific not only to the demands of the task (Hunter, 2014; Hunter, Duchateau,
et al., 2004) but is specific to the muscle group involved. Some muscle groups such as the
ankle dorsiflexors are less susceptible to sex and age differences (Avin & Law, 2011) but
as shown in this dissertation, they may also be less susceptible to cortical inputs
manipulated with cognitive demand.
Although fatigability was not altered with imposed cognitive demand in young or
older adults, variability in the time to failure across sessions was greater for the older
adults than the young adults. The greater variability in performance of maximal
contractions with advanced age can be due greater variability in cortical and motor nerve
activation during motor tasks (Hunter, Todd, et al., 2008; Yoon et al., 2008). The results
in this dissertation show that this age-related variability is particularly large during
submaximal task of the lower limb and that cognitive demand will exacerbate this
variability for a participant and between old adults. These results indicate cortical
involvement in the greater variability possibly due to reduced inhibition onto the
123
motoneurone pool. For example, older adults had greater soleus muscle activation during
the high-cognitive demand session relative to the other sessions and compared with the
young adults (study 3, aim 2): this suggests less inhibition of the antagonist muscle from
descending cortical sources (Baudry et al., 2010). Age-related changes along the neuraxis
can also result in increased antagonist muscle activation (Hortobagyi & DeVita, 2006;
Macaluso et al., 2002), which may be a response to decreased intracortical inhibition that
appears to only occur in older adults as a consequence of the increased demands place on
the remodeled neuromuscular system (Plow et al., 2013). Future studies that involve
assessment of cortical inhibition [which can be done with paired pulse cortical
stimulation (eg., McGinley et al., 2010)] with and without cognitive demand during a
motor task may provide insight into the role of age-related cortical inhibition and its
contribution to the increased in motor variability among older men and women.
The results of this dissertation contribute important information to the current
body of literature regarding the role of the cortex in force production and control in the
lower limb. Understanding the key cortical areas associated with force control in the
lower leg in healthy young adults, and the influence of factors that increased descending
drive from the cortex, such as increased cognitive demand or fatigue, establishes a
foundation for identifying the plasticity of these areas with impaired motor function that
can occur with neurological conditions and aging as well as enhanced function that is
possible with physical exercise in all populations.
These results also provide evidence that increased involvement of motor and nonmotor cortical areas can disrupt motor performance of low-to-moderate intensity
isometric contractions in the lower limb more so in older adults than young adults. These
124
results have important performance implications for cognitively demanding and low-tomoderate-force tasks that are common to daily function in older adults. Key cortical and
subcortical brain areas associated with control of lower limb steady contractions were
also identified in healthy men and women and could be targeted in future studies to better
understand the influence of cortical control on neuromuscular impairments such as those
that occurs with neurologic conditions as well as normal and pathologic aging.
Moderate, regular exercise (Granacher et al., 2010; Krebs et al., 1998) and practice
(Marmon, Gould, et al., 2011; Silsupadol et al., 2009; Voelcker-Rehage & Alberts, 2007;
Yogev-Seligmann et al., 2008) may reduce variability in motor performance and
encourage efficient adaptive allocation of cognitive resources (Schaefer & Schumacher,
2011) to better ensure successful task performance. In a clinical setting, it may be a
valuable use of time to evaluate the cost of dual tasking through the use of clinical tests
(such as Stops walking while talking, and walking while performing mental math or
verbal fluency tasks)(Yogev-Seligmann et al., 2008) and utilize practice to ameliorate the
effects of dual tasking particularly in older adults because they have been shown to
improve performance with practice (Yogev-Seligmann et al., 2008).
The information provided in this dissertation can be helpful in understanding
cortical contributions to force control in the lower extremity, although many questions
still remain. The adult neuromuscular system undergoes significant changes with advance
age, and acquiring a better understanding of the associated neural processes and cortical
control can help with design of recovery programs from conditions that impact both
cognitive and motor function such as stroke. Examples of age-related changes in cortical
activity include decreased lateralization in M1 activity during motor task performance
125
(Langan et al., 2010) and the prefrontal cortex during cognitive performance (Cabeza,
2002) found in older adults compared with young adults. These changes can have
implications regarding the compensatory strategies older adults utilize in order to perform
the same tasks as young adults. Further, because changes in cognitive demand have been
demonstrated to influence motor performance in the older adult, understanding the
cortical regions associated with dual-task performance in the lower limb of young and old
men and women may provide valuable information that can be utilized to improve
functional task performance.
126
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